Guardant Health introduces Guardant GalaxyTM suite of advanced AI analytics to enhance its portfolio of cancer tests and accelerate biomarker discovery
Guardant Health (NASDAQ: GH) has unveiled Guardant Galaxy, a suite of AI-powered analytical technologies aimed at enhancing cancer testing and drug discovery. The first application, developed in collaboration with Lunit, improves biomarker detection by over 20% for the Guardant360 TissueNext PD-L1 test in non-small cell lung cancer cases. Future applications will focus on deep learning-driven genomic and epigenomic discovery, leveraging real-world clinical data from the GuardantINFORM platform. The initiative aims to provide oncologists with precise information and to accelerate biomarker discovery.
- Launch of Guardant Galaxy suite enhances analytical capabilities.
- Collaboration with Lunit improved biomarker detection by over 20%.
- Future applications include deep learning analytics for genomic discovery.
- None.
- Suite of AI-powered expert systems and discovery platforms will be developed internally and through outside partnerships
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First application using AI-backed image analysis developed by Lunit® for Guardant360 TissueNextTM PD-L1 test improves biomarker detection by >
20% in NSCLC - Future planned applications include deep learning-driven genomic, epigenomic and spatial biomarker discovery via collaboration with biopharma partners and integration with GuardantINFORM™ real-world clinical data platform
“Precision medicine has clearly demonstrated its potential to improve outcomes for patients with cancer, and our genomic profiling tests play a key role by identifying cancer biomarkers and other factors that can inform diagnosis and therapy selection,” said
The first application in the Guardant Galaxy suite is an AI-backed digital pathology platform developed by Lunit®, a
“We are excited to partner with
In addition to enhancing current standard-of-care biomarker interpretation, early Guardant Galaxy applications will also include novel native AI analysis applications, including the prediction of patient response to immunotherapy based on the spatial distribution of immune, stromal and tumor cells in standard H&E-stained slides.
Additional planned applications include enhanced oncology drug and biomarker discovery and development capabilities based on mining the extensive genomic and epigenomic data produced by Guardant Health’s diagnostic tests, cross-referenced to real-world outcomes data available through the GuardantINFORM data platform.
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Forward-Looking Statements
This press release contains forward-looking statements within the meaning of federal securities laws, including statements regarding the potential utilities, values, benefits and advantages of Guardant Health’s liquid biopsy tests or assays, which involve risks and uncertainties that could cause the actual results to differ materially from the anticipated results and expectations expressed in these forward-looking statements. These statements are based on current expectations, forecasts and assumptions, and actual outcomes and results could differ materially from these statements due to a number of factors. These and additional risks and uncertainties that could affect Guardant Health’s financial and operating results and cause actual results to differ materially from those indicated by the forward-looking statements made in this press release include those discussed under the captions “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operation” and elsewhere in its Annual Report on Form 10-K for the year ended
*The Guardant360 TissueNext PD-L1 test with enhanced biomarker detection from Guardant Galaxy is not available as a standalone test; it is automatically included when physicians order the Guardant360 CDx test with the Guardant360 TissueNext and PD-L1 test for NSCLC.
References
1. Choi S. et al. Artificial intelligence–powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non–small cell lung cancer with better prediction of immunotherapy response. Eur J Cancer 2022;170:17-26.
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