Cofactor Genomics launches ImmunoPrism kit for use in clinical sequencing laboratories

Share on facebook
Share on twitter
Share on linkedin
Share on email
Share on print

Cofactor Genomics has launched an RNA-based immune profiling kit developed for laboratories wishing to derive the immune composition of tumor samples.

Using the ImmunoPrism Immune Profiling Kit, laboratories now have access to the same kit Cofactor Genomics uses to prep, sequence and analyze against Cofactor’s database of machine-learning optimized immune reference expression models.

The launch of the kit follows recent ImmunoPrism announcements on collaborations with The Fred Hutchinson Cancer Research Center and NCI, and most recently, the clinical accreditation of the assay by the College of American Pathologists within Cofactor’s CAP/CLIA lab.

Building on data from thousands of RNA expression profiles, the fully analyzed, proprietary, biomarker discovery report includes quantitative immune cell characterizations and enables intra- and inter-sample immune cell ratios and comparisons, which have been shown to have prognostic value. The report also includes statistics such as p-value, threshold for patient selection, predictive accuracy, and positive/negative predictive values, the company said.

Cofactor’s ImmunoPrism Immune Profiling Kit details the quantitative percentage for eight major immune cell types and expression levels of ten immune escape genes. This immune characterization can be obtained using FFPE, FNAs, CNBs, accommodating solid tumors with very limited amounts of tissue, in some cases as low as 20 nanograms. This includes pre-treatment clinical samples, which previously have been difficult to characterize, the company said.

Table of Contents

YOU MAY BE INTERESTED IN

When our hematological malignancy testing pilot project began in Eldoret, Kenya, there seemed to be a mismatch in relation to progress in healthcare. The region, like much of sub-Saharan Africa, had been focusing on combatting infectious diseases such as HIV and malaria—which was much-needed—yet cancer care was under-resourced. 
Artificial intelligence is rapidly transforming biomedical research and healthcare. Large language models, foundation models, and AI agents are increasingly being deployed to assist with data interpretation, literature review, clinical decision support, and translational research. 
In modern oncology, important insights from clinical trials often emerge years after initial publication. As new therapies extend survival and transition more patients into long-term remissions, clinicians and researchers are increasingly looking beyond initial response rates to understand durability, long-term safety, and even the possibility of a cure. 

Never miss an issue!

Get alerts for our award-winning coverage in your inbox.

Login