Hamburg-based Indivumed specializes in using the highest quality biospecimen and comprehensive clinical data to advance research and development in precision oncology. Its IndivuType discovery solution uses AWS to store data and support analysis to decipher the complexity of cancer. By improving its AWS infrastructure, Indivumed has saved more than 50 percent on total IT costs and ramped up the number of samples it can process from 20 to 500 a week, a 2,400 percent increase.

“We have the most highly automated multi-omics processing facility out there. It’s driving the creation of new treatments that will ultimately save and extend people’s lives. That’s something to be proud of.” Rene Steen, Vice President for IT, Indivumed

Indivumed Boosts Cancer Research With Powerful Analytics Built on AWS

For two decades, Hamburg-based Indivumed has specialized in biobanking, providing infrastructure, expertise, and technology for cancer research and development. Most of its customers and partners are academic research institutes and pharmaceutical companies that use the insights Indivumed generates to discover and validate novel drugs and ultimately develop new treatments for life-threatening cancers.

With the life sciences field and pharmaceutical industry becoming more data-driven, Indivumed saw an opportunity to generate these insights through analyzing multi-omics data. Indivumed decided to use the thousands of tissue samples it stores to create a unique repository for deep molecular information on cancers.

But the datasets are complex and extensive. To manage this complexity, the company turned to Amazon Web Services (AWS) and used cloud-based high performance computing (HPC) to build the world’s first and most extensive proprietary multi-omics database.

Launching a Multi-Omics Database on AWS

The result was IndivuType, a multi-omics database that combines diverse molecular biological information with clinical information from thousands of patients across Europe, the US, and Asia. The datasets for each cancer sample—including raw readouts from the molecular assay, which detects markers of disease—can reach 200 GB in size.

Indivumed knew its compute requirements would be significant. So it decided to build an HPC cluster that could not only handle huge datasets, but also scale resources up and down automatically based on the amount of processing required.

It chose AWS to help make its vision a reality. “AWS was the best choice to help us scale and it provides a range of secure, reliable, and serverless technologies for us to build on,” says Dr. Jonathan Woodsmith, vice president of advanced analytics and AI at Indivumed.

Initially, Indivumed built an HPC cluster using Amazon Elastic Compute Cloud (EC2), which provides secure and resizable compute capacity, and Amazon Elastic File System (EFS), which automatically grows and shrinks as files are added and removed.

Modernizing Cluster Increases Processing Capacity by 2,400%

As the company grew, Indivumed needed to ramp up the amount of data it could handle so that it could increase the number of samples it could process each year. To achieve this, Indivumed needed to refactor the cluster. “We spent a significant amount of time building a cloud-native tech platform,” says Woodsmith…

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