On November 16, 2020 Champions Oncology, Inc. (NASDAQ:CSBR), a leading global oncology technology solutions provider that is transforming drug discovery through innovative pharmacology, biomarker and data platforms, reported the launch of its new proprietary SaaS program known as Lumin Bioinformatics (Press release, Champions Oncology, NOV 16, 2020, View Source [SID1234574975]). By leveraging Champions’ large cancer dataset, which is unique for its substantial depth of characterization, combined with many other published cancer datasets, Lumin provides users with a vast database of tumor characterization that can be used for computational research. The software, which is purchased as a yearly subscription, also provides sophisticated analytic tools that are utilized to interrogate the data for associations between molecular and various phenotypic features, including thousands of drug responses, clinical annotations, prior treatment histories, and genetic dependencies. Insights developed using Lumin can provide the basis for biomarker hypotheses, reveal potential mechanisms of therapeutic resistance, and guide the direction of additional preclinical evaluations.
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Ronnie Morris, MD, President and CEO, said "The launch of Lumin Bioinformatics is an exciting step forward in our strategy to deploy the unique dataset that we have established over the past decade. Our data is distinct in its depth of characterization, providing a powerful foundation for computational analyses. Lumin harnesses this power and facilitates access to it for a deeper understanding of therapeutic programs and tumor cell biology. We’re excited to see how it can impact the direction of our customers’ research programs."
"Lumin Bioinformatics overcomes many of the computational challenges our customers experience", said Michael Ritchie, PhD, MBA, Chief Commercial Officer at Champions Oncology. "Most of the biopharma organizations we work with do not have large bioinformatic teams, nor access to such a robust dataset. Lumin fills these gaps by making extremely complex computational analyses accessible to a broader audience."