Introducing MI GPS Score – Powered by Caris Next Generation Profiling (NGP)

Caris has the largest and most comprehensive database of combined molecular and clinical outcomes data in the world, and we are actively employing advanced machine learning capabilities with the database to identify unique molecular signatures. These molecular signatures can be used to better identify cancer subtypes and predict patient response to certain therapies. Based off this work1, we are pleased to introduce MI GPS Score – a tool to help manage cancer of unknown primary (CUP) or cases identified by the ordering physician with atypical clinical presentation or clinical ambiguity.

MI GPS Score provides a tumor type similarity score that compares genomic characteristics of the patient’s tumor against the Caris database, in conjunction with a comprehensive pathology consultation (e.g. lung cancer tumor submitted for testing has a similar molecular signature as the lung cancers found in the Caris Database, or conversely the molecular signature is not similar to lung cancer, but similar to another tumor type’s molecular signature).

MI GPS Score will be performed and reported for all CUP cases and can be added to any solid tumor order by selecting the appropriate box on the req. Results for the MI GPS Score will be used with a Caris pathology consultation and populated onto the final Caris report. These results will provide additional insight by assessing how closely tumors match the genomic signatures of tissue types to help you make more informed treatment decisions.

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

Caris Next Generation Profiling (NGP) uses the power of DEAN (Deliberation Analytics) artificial intelligence and machine learning technology to provide oncologists with the most thorough genomic and molecular analysis classification to inform decision making. Caris NGP analyzes historical clinical and outcome data and learns from the past to provide for a better future via molecular and genomic subtyping.

 

  1. Machine learning algorithm analysis using a commercial 592-gene NGS panel to accurately predict tumor lineage for carcinoma of unknown primary (CUP). Jim Abraham, Amy B. Heimberger, Zoran Gatalica, Wolfgang Michael Korn, and David Spetzler. Journal of Clinical Oncology 2019 37:15_suppl, 3083-3083. Published online May 26, 2019.