Cascadia Data Alliance

Data Science Collaborations

THE OPPORTUNITY: The Cascadia region of North America is home to some of the world’s leading technology, research, and medical organizations. Scientific collaboration across these research institutions will accelerate research — at the center of this collaboration is a focus on the coordinated generation of data and effective sharing. Development of robust regional collaborations and a data sharing ecosystem will position the Pacific Northwest as a global innovator in biomedical research and healthcare, now and into the future. 

THE CHALLENGE: Cultural, technical and policy barriers have historically limited cross-organizational data sharing. The Cascadia Data Alliance will change that.

Cascadia Data Alliance logo

The Cascadia Data Alliance created the Cascadia Data Discovery Initiative (CCD) to spur regional cross-institutional collaboration.  As a result of CDDI’s efforts, Cascadia has funded pilot projects and partnered with the Specimen Acquisition Data Network to accelerate data sharing.


Cascadia Region of North America
Cascadia Region of North America

Cascadia funding opportunity - Microsoft Azure computing credits. Available until 2/4.

Regional Collaboration

Three pilot projects have received funding that: (1) answer an important scientific question that benefits from regional collaboration; and (2) develop or test innovative approaches to using technical solutions, data/methods standardization, best practices, and/or cloud services (using Microsoft Azure) that can be generalized to future collaborative efforts. These projects are made possible thanks to financial and in-kind support from Microsoft and institutional support from each participating organization.

scientist viewing data from cloud

Cloud-Based Genome Analysis for Monitoring Breast Cancer

A breast tumor’s genetics can change as the cancer progresses throughout treatment. But the changes don’t happen universally throughout all cells in the tumor, leading to distinct populations of cancer cells. If some of the cell populations have mutations that confer drug resistance, this can become a serious problem for the patient. In this new project, the research team will use state-of-the-art genomic methods to determine how tumor cells’ genetic profiles change during treatment and how those changes are linked to the patient’s outcomes.

Computational biologist Dr. Gavin Ha of Fred Hutch is one of the project’s leaders, along with BC Cancer’s Drs. Andrew Roth and Samuel Aparicio, and Dr. Natasha Hunter of the University of Washington Medicine and Seattle Cancer Care Alliance.

Research technician working in a lab under a safety hood

How the Gut Microbiome Affects Cancer Immunotherapy Drugs

Drugs called checkpoint inhibitors that release the power of the immune system against tumors are important treatments for many different types of cancer. But the drugs’ effectiveness and side effects vary from patient to patient — and research has shown that the communities of bacteria in a patient’s body, or microbiome, plays a role. But which bacterial species are most important? A team of scientists hopes to find out. They will create a large, multi-institutional repository of samples of mouth tissues and stool from people who are using checkpoint inhibitors for cancer treatment.

The researchers — led by Fred Hutch’s Dr. David Fredricks, BC Cancer’s Dr. Kerry Savage and OHSU’s Dr. Morgan Hakki — will harness cutting-edge genomic technologies, data analysis tools and cloud computing to reveal the genetic fingerprints of the bacteria in the gut and match them to the outcomes of the patients’ treatment.

graphic representing machine learning

Using Machine Learning to Help Diagnose Specific Cancer Types

As targeted treatments have become available for specific types of ovarian cancer, it’s become more important than ever that each patient receives an accurate diagnosis of her tumor type. To help make this possible, a research team wants to establish an international network for AI-based, privacy-protected pathology quality assurance. Ovarian cancer will be the team’s proof of concept for a system that could eventually be used for a variety of cancers.

The team is led by Fred Hutch’s Dr. Holly Harris, BC Cancer’s Dr. David Huntsman, OHSU’s Dr. Terry Morgan and Dr. Ivan Beschastnikh, a computer scientist at UBC. It also includes Simon Fraser University's Dr. Tania Bubela and UBC's Dr. Ali Bashashati, who developed the original algorithm the group hopes to generalize. 

Microsoft Azure Cloud Credits

This is a Cascadia funding opportunity for new regional collaborations in data-intensive science.

Microsoft Azure computing credits are available for interdisciplinary regional collaborations between Fred Hutch, BC Cancer, UBC Data Science Institute, UW e-Science Institute and OHSU Knight Cancer Institute. Scientific projects involving basic, clinical, and public health research will be equally considered, particularly if novel collaborations and approaches are proposed. Projects that involve sharing of data sets across institutions and/or projects that validate new computational tools or build on existing tools are encouraged. Cancer research and infectious disease research, including COVID-related proposals, are welcomed. All areas of research across participating institutions will be equally considered.


  • Use Azure cloud services to support important basic, clinical, and/or public health research that benefits from regional collaboration.
  • Credits may be used to develop or test an innovative approach, to move existing workflows into the cloud, and/or to take advantage of technical and computational resources in Azure to advance project work.
  • Projects may be new to using cloud computing resources or may already be using Azure or other cloud computing resources.


  • The project must have multiple Principal Investigators (PIs).
  • At least one PI should be from one of the primary participating institutions: Fred Hutch, BC Cancer, OHSU Knight Cancer Institute, University of Washington (UW) eScience Institute, or UBC in order to access Azure credits.
    • All PIs and collaborators must be located at Pacific Northwest research institutions to apply.
  • Collaborations where at least one PI is at the Assistant and Associate rank (or equivalent) are encouraged, as are collaborative projects between junior and senior investigators.

For more information, please review the RFA:

Cascadia Azure Credits RFA


Applications due by 11:59pm on February 4, 2022.



Data Sharing

CDDI partners and the Specimen Acquisition Network created a shared data and material use agreement. The Umbrella Agreement provides a streamlined process and easy-to-use templates to accelerate sharing of data and specimens across signatory organizations in the Pacific Northwest.

Preserving Privacy to Enable Data Access

Cascadia is also working to test data sharing methods that contain oftentimes sensitive information in a way that provides strong privacy preservation. Cascadia partners are working together to develop and demonstrate technology solutions that both preserve privacy and facilitate meaningful analysis of health research data.


  • Resources for Cascadia regional collaboration
  • Closer ties to regional collaborators with complementary skills and expertise (and access to their broader networks)
  • Reduction in red tape to accelerate data sharing

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