Microsoft Azure (Microsoft) > Case Studies > Adaptive Biotechnologies Leverages Azure for Immune Medicine Platform

Adaptive Biotechnologies Leverages Azure for Immune Medicine Platform

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Technology Category
  • Analytics & Modeling - Machine Learning
  • Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
  • Education
  • Equipment & Machinery
Applicable Functions
  • Product Research & Development
  • Quality Assurance
Use Cases
  • Automated Disease Diagnosis
  • Predictive Maintenance
Services
  • Data Science Services
  • Training
About The Customer
Founded in 2009, Adaptive Biotechnologies is a commercial-stage biotechnology company based in Seattle, Washington. The company is focused on harnessing the inherent biology of the adaptive immune system to transform the diagnosis and treatment of disease. Adaptive Biotechnologies has built a proprietary immune medicine platform that is capable of decoding the genetic language of the adaptive immune system at scale. The company's innovative approach involves sequencing the genomes of immune cells to read the immune systems of individuals and across the population. Adaptive Biotechnologies has a dynamic clinical immunomics database that includes more than 47 billion immune receptors.
The Challenge
Adaptive Biotechnologies, a commercial-stage biotechnology company, has been working on harnessing the biology of the adaptive immune system to transform disease diagnosis and treatment. The company has built a proprietary immune medicine platform that decodes the genetic language of the adaptive immune system. However, the challenge was to synthesize this vast system of biology and tap into the full value of the massive clinical immunomics database generated. The company needed high-scale compute resources and machine learning capabilities to unlock the full potential of the research data. The immune system's complexity and the sheer volume of data generated by Adaptive’s dynamic clinical immunomics database, which includes more than 47 billion immune receptors, posed significant challenges.
The Solution
Adaptive Biotechnologies turned to Microsoft Azure for compute, storage, and machine learning capabilities. The company adopted Azure to apply machine learning to exponentially accelerate its ability to gain novel insights from its clinical immunomics database. The platform was used for end-to-end support, from sample intake to processing, to generate the sequencing data targeted to the immune response of a particular genome. The data was then processed through an automated in-house bioinformatics pipeline running on virtual machines created using Azure Virtual Machines. Adaptive researchers used Azure Machine Learning and Azure Kubernetes Service (AKS) to train and evaluate models. The company stored its many terabytes of source and experimental data in Azure Blob storage and used virtual machines and Azure Application Gateway to provide web-based access to data and interactive research environments.
Operational Impact
  • Adaptive Biotechnologies has transformed its ability to process and analyze massive amounts of data on a whole new scale. The company is not only doing faster research and processing a higher volume, but it is also finding results that it could not even look for before. The use of Azure has enabled Adaptive to develop a cloud-first workbench, allowing its geographically distributed data science teams to rapidly explore the data and develop and train new models. The company can now process datasets simultaneously, and the accuracy of its results is continuously improved and updated online in real time. This has led to faster results, which are crucial for patients waiting for life-changing news, such as a cancer test result or new treatment for a disease.
Quantitative Benefit
  • Adaptive processed 500 million TCR sequences within weeks to identify TCR signatures of infection.
  • The company has been able to cut hours off the pipeline processing time.
  • Adaptive has fundamentally increased its productivity and is processing a higher volume of data.

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