Natural State Laboratories Chooses SOPHiA GENETICS to Help Launch their In-House Hereditary Cancer Testing

SOPHiA GENETICS technology will help increase cancer research abilities

BOSTON and LAUSANNE, Switzerland, May 16, 2023 /PRNewswire/ -- SOPHiA GENETICS (Nasdaq: SOPH), a cloud-native software company in the healthcare space and a leader in data-driven medicine, today announced that Natural State Laboratories, a xfull-service, state-of-the-art molecular laboratory based in Little Rock, Arkansas, has chosen SOPHiA GENETICS technology to help them expand their cancer testing and research capabilities. Natural State Laboratories will soon begin using SOPHiA DDM™ to launch in-house testing for hereditary cancers.

The SOPHiA DDM™ technology will streamline and expedite analysis and interpretation workflows for Natural State Laboratories, increasing the volume of genomic profiles analyzed. With the in-house analysis and increased testing speed, Natural State Laboratories will be able to serve a much wider population in the region.

"At Natural State Laboratories, we believe that the future of cancer is in how quickly we are aware of the presence of and precursors to cancer," said Ruslana Tytarenko, MS, BSc, MLS (ASCP)CM, Lab Technical Supervisor. "The SOPHiA DDM™ Platform will increase the volume of research and testing we're able to conduct, helping to more quickly analyze more genomic profiles and identify those who are pre-dispositioned to hereditary cancers."

Hereditary cancer is responsible for roughly 10 percent of all diagnosed cancers¹. The ability to identify a pre-disposition to hereditary cancers can help clinical researchers with the formulation of future treatment plans. And, while testing is somewhat simple via next-generation sequencing (NGS), which is prevalent in the detection of biomarkers for hereditary cancers, the amount of data resulting from NGS is vast and complex.  The SOPHiA DDM™ Platform uses artificial intelligence and machine learning with patented technologies to analyze raw NGS data, streamlining it for simplified interpretation, and expedited reporting.

"The development of Natural State Laboratories' new hereditary cancer test, using the SOPHiA DDM™ technology, will continue furthering our work to democratize data-driven medicine," said Ken Freedman, Chief Revenue Officer, SOPHiA GENETICS. "With this new test, Natural State Laboratories will have an in-house database that allows them to increase efficiency and better serve a wider population. We're excited to be part of their next step in cancer testing and research for the surrounding population."

For more information on SOPHiA GENETICS, visit SOPHiAGENETICS.COM, or connect on Twitter, LinkedIn, Facebook, and Instagram. 

About SOPHiA GENETICS 
SOPHiA GENETICS (Nasdaq: SOPH) is a software company dedicated to establishing the practice of data-driven medicine as the standard of care and for life sciences research. It is the creator of the SOPHiA DDM™ Platform, a cloud-native platform capable of analyzing data and generating insights from complex multimodal data sets and different diagnostic modalities. The SOPHiA DDM™ Platform and related solutions, products and services are currently used by a broad network of hospital, laboratory, and biopharma institutions globally. For more information, visit SOPHiAGENETICS.COM, or connect on Twitter, LinkedIn, Facebook, and Instagram. Where others see data, we see answers. 

SOPHiA GENETICS products are for Research Use Only and not for use in diagnostic procedures unless specified otherwise. The information in this press release is about products that may or may not be available in different countries and, if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please contact [email protected] to obtain the appropriate product information for your country of residence.

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¹ Al Harthi, F. S., et al. (2020) 'Familial/inherited cancer syndrome: a focus on the highly consanguineous Arab population', npj Genomic Medicine, 5, 3

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