Technical University of Denmark Uses TigerGraph to Help Find More Effective Treatments for Acute Lymphoblastic Leukemia

REDWOOD CITY, Calif., March 30, 2021 – TigerGraph, provider of the leading graph analytics platform, has announced that the Technical University of Denmark (DTU), a leading university in the areas of technical and natural sciences, is using TigerGraph’s advanced graph analytics with machine learning and AI techniques to improve the treatment of acute lymphoblastic leukemia.

Researchers at DTU are part of a major project across Denmark and Sweden to map genetic material for everyone with childhood cancer. As part of a larger collaboration through the EU-funded iCOPE (Interregional Childhood Oncology Precision Medicine Exploration), the process starts with patient blood tests that through Whole Genome Sequencing (WGS) paired with RNA-seq expression data are used to find aberrant expression patterns correlated or possibly caused by enhancer mutations. The long term goal of iCOPE is to improve diagnostics, treatment, cure rates, and the overall life situation of children with cancer.

This process generates enormous amounts of data that using TigerGraph will be linked together with various other data points about the patient’s life, illness, and treatment in order to understand to a much greater extent why children get cancer, provide earlier diagnosis and far more effective treatment.

As Jesper Vang,  PhD Student, Department of Health Technology, Cancer Systems Biology at DTU explains, “Our current system only hosts raw data such as genotype and whole-genome sequenced data. This raw data is run through a custom pipeline that calls genetic variants and annotates the data in a MySQL DB.  However, we needed something easier to work with specifically for the clinical personnel, that also allows them to look up genetic associations which is a perfect use case for graph analytics”.

DTU opted for an on-premise graph database platform that would deliver the required performance and evaluated a number of options, in particular, Neo4j but concluded that only TigerGraph could scale and provide the analytical depth the project required.  “In our testing, Tigergraph was the only solution offering the highest performance with the ability to scale to the levels we will eventually need,” explains Vang.

DTU is in the final stages of bringing the full system online and it is already being used in a specific project that combines the fields of AI, machine learning and translational bioinformatics to create models that can predict the risk of relapse and toxicity within acute lymphoblastic leukemia treatments.

Martin Darling, VP EMEA at TigerGraph added, “The work at DTU and across the wider iCOPE project is transformational and highlights how the application of clinical excellence with innovative technologies can unlock breakthrough insights within areas such as life sciences. We are delighted to have Jesper present a full case study on the project at the upcoming Graph + AI Summit this April.”

About the Technical University of Denmark (DTU)

DTU was founded in 1829 and has ever since been dedicated to developing the natural and the technical sciences with a view to creating value for society. During the past two centuries, DTU has undergone a transformation from a 19th-century polytechnical institution to a present-day international, multidisciplinary institution that caters for research, teaching, and collaboration across scientific, professional, and geographical borders.

About TigerGraph  

TigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industry’s first and only distributed native graph database, TigerGraph’s proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. The company is headquartered in Redwood City, California, USA. Start free with tigergraph.com/cloud. 

Source: Amanda Morris, TigerGraph