BOSTON– Genialis, a leader in applied data science for the development of precision medicines, today announced it will leverage its proprietary ResponderID AI platform technology to provide retrospective analysis to customers and collaborators to help classify patients for targeted therapy using the OncXerna Xerna™ TME Panel, an RNA-based pan-tumor biomarker shown to be predictive of responses to multiple immune-targeted cancer therapies.
Targeted therapies today provide benefit only to a fraction of the patients for a given disease indication because current laboratory models do not faithfully represent patient biology. ResponderID starts at the end—with the patients. The platform’s computational models of disease biology embrace the complexity that defines each patient. The TME Panel measures approximately 100 genes by RNA sequencing and applies machine learning to decipher the therapeutically relevant patterns in those data. The TME Panel can enrich patient response to different classes of therapy (e.g. immune checkpoint inhibitors and anti-angiogenics) in several cancer types, including gastric and ovarian.
“Inclusion of the TME Panel algorithm within ResponderID will create a valuable starting point for numerous potential research and commercial partnerships,” said Rafael Rosengarten, Ph.D., CEO of Genialis. “By offering our customers and collaborators the opportunity to quickly and comprehensively analyze their retrospective data, together we will be able to define and implement the most effective precision medicine strategy.”
Collaborators that wish to use the Xerna TME Panel for future clinical studies and/or prospective research will be referred to one of OncXerna’s commercial diagnostic partners.
“We have a mounting body of evidence that suggests the Xerna TME Panel may be applicable to virtually any solid tumor type, and can be helpful to find the right patients for drugs that target the immune system,” commented OncXerna CEO Laura Benjamin. “Genialis has the opportunity to evaluate the TME Panel across many of these disease types and for different therapeutic mechanisms.”
ResponderID defines, models and validates disease models for drug development and discovery programs. The platform is a technology suite for clinical and translational research, built from years of experience working with partners across the industry and advanced internal R&D. ResponderID incorporates technologies and proprietary tools for feature selection, data harmonization, machine learning modeling and interpretation of the models. ResponderID is already in use to improve the efficiency, effectiveness and success rate of drug development and discovery across the oncology space, with more therapeutic areas in the works. Through numerous collaborations, ResponderID is building computational models to help treat cancers like gastric, colorectal, ovarian, lung, melanoma, acute myeloid leukemia, mesothelioma, and others.
