Cofactor Genomics Opens Enrollment for Non-small Cell Lung Cancer Study of OncoPrism Predictive Diagnostic Assay

By better identifying patients who will likely respond to immunotherapy, Cofactor’s OncoPrism will spare more patients from chemotherapy and its negative side effects. The low input of tissue required for the test also makes it easier for lung cancer patients in whom a core needle biopsy is the safest option for acquiring tissue. Published results in Nature Scientific Reports show that Cofactor’s approach predicted patient response to anti-PD-1 therapy in lung cancer and outperformed the indicated PD-L1 test and Tumor Mutational Burden.

The PREDAPT (Predicting Immunotherapy Efficacy From Analysis of Pre-treatment Tumor Biopsies) Trial is evaluating use of the OncoPrism assay in effectively predicting a patient’s response to immunotherapy. To date, more than 20 healthcare systems have partnered with Cofactor in the study that will ultimately study 11 solid tumor cancers. The first indication opened for study was recurrent and metastatic squamous cell carcinoma of the head and neck (RM-HNSCC). HNSCC data presented at the AGBT Precision Health Meeting showed that Cofactor’s test is nearly twice as accurate as PD-L1 assays in determining patients that will benefit from immunotherapies, such as Keytruda (pembrolizumab) or Opdivo (nivolumab). Indications to be studied within the PREDAPT Trial include triple-negative breast, cervical, colorectal, esophageal, gastric, head and neck, kidney, liver, lung, and urothelial cancers.

“Lung cancer is the deadliest cancer in America, yet today less than 25 percent of cancer patients are matched to an immunotherapy that helps them. Our mission is to address that gap by leading the development of predictive diagnostics that can be a more effective matchmaker between patients and the treatments most likely to benefit them,” said Sara Lapomarda, Director of Clinical Partnerships for Cofactor Genomics. “By studying our unique diagnostic approach in lung cancer, we hope to positively impact the trial-and-error approach to treating patients that can delay time to an effective treatment and spare them from unnecessary toxicities from chemotherapy in order to improve patient outcomes and survival for many. We intend to grow our clinical and biomarker team quickly to expedite validation of our lung cancer assay so it will closely follow our head and neck cancer assay in 2023.”

About Predictive Immune Modeling

Predictive Immune Modeling (PIM) is Cofactor’s proprietary approach to immunotherapy predictive diagnostics. It is based on understanding the immune cell composition of a tumor, such as T cells, which has been shown to be predictive of immunotherapy response. It goes beyond testing individual markers by first building a model of the immune composition of patient responders to immunotherapy using 100+ RNA-based biomarkers processed with machine learning. Then, the company’s OncoPrism assay is used to compare a patient’s genetic profile to this model to determine how likely the patient is to be a responder themselves. This approach has been shown in published literature to be almost twice as accurate as the most commonly used biomarker, PD-LI, in identifying immunotherapy responders across a variety of cancers.

About Cofactor Genomics

Cofactor Genomics is bridging the precision medicine gap by building diagnostic tools to match the right patient to the right treatment at the right time. Predicting patient response to therapy currently relies on isolated, single-analyte biomarkers that have failed to deliver accurate therapy response predictions, resulting in unnecessary healthcare costs, and most harmfully, negative outcomes for patients. Cofactor’s products use Predictive Immune Modeling, which leverages RNA data and machine learning to combine biological signals, creating multidimensional biomarkers to deliver on the promise of precision medicine. Cofactor Genomics offers its full-service molecular, informatic, and database tools to make drug discovery, clinical trials and therapy selection more successful.

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