Cofactor Genomics, San Francisco, has launched a grant program that will provide researchers with early access to a new feature in the company’s Predictive Immune Modeling platform. The ‘Functional to FFPE’ early access grant program is intended to support solid tumor or engineered cell therapy exploratory studies, with the company covering up to $100,000 in reagents, sequencing, and analytical support.

Cofactor’s Predictive Immune Modeling platform enables researchers to perform cell state characterization for formalin-fixed, paraffin-embedded (FFPE) tissue samples. Measuring cell states, such as T cell exhaustion, is considered important for improving the accuracy of patient selection for treatments using immune checkpoint inhibitors. Nevertheless, the field has so far failed to reach consensus on a short list of individual analytes that effectively characterize exhaustion using existing technologies. Cofactor is addressing the challenge using multidimensional biomarkers that are designed to be more accurate than single markers.


Jon Armstrong, Cofactor Genomics.

Cofactor’s platform is supported by a proprietary database of health expression models, built using machine learning to identify multidimensional gene expression patterns that make possible highly sensitive characterization of cell types in heterogenous tissue or cell samples. With this new feature, the database has been expanded to include validated models for the characterization of unstimulated T cells, activated T cells, and exhausted T cells.

The presence of terminally exhausted T cells has been shown to predict the failure of therapies that make use of anti-PD-1 checkpoint inhibitors. As a result, the ability to detect cell states has garnered significant interest, but has proven difficult to accomplish when using archived clinical specimens. Previously, only viable tissue and laborious functional assays were able to generate difficult-to-interpret rough approximations of cell state.

“We have to move beyond just detecting cells, and better characterize how those cells are influenced by their microenvironment,” says Jon Armstrong, chief scientific officer at Cofactor. “The ability of RNA-based models to accurately characterize these important cell states in FFPE tissue samples is exactly the reason why Cofactor has invested in Predictive Immune Modeling. No other technology—genomic or proteomic—has been able to accomplish this in FFPE.”

For further information, visit Cofactor Genomics.