Predictive Test Improves Immunotherapy Results for Cancer Patients

However, only a select group of patients have found success with these newer immunotherapies, which utilize the patient's immune response to combat cancer. Currently, predicting a patient's response to therapy depends on isolated, single-analyte biomarkers, which have proven inadequate in accurately predicting therapy responses. This inefficiency leads to unnecessary healthcare expenses and, more critically, negative patient outcomes. Now, a new test, that utilizes an advanced multidimensional immune biomarker can predict which cancer patients are likely to respond to monotherapy of immune checkpoint inhibitors like Keytruda.

Cofactor Genomics (San Francisco, CA, USA) employs Predictive Immune Modeling – a technique that utilizes RNA data and machine learning to combine biological signals, thus creating multidimensional biomarkers that fulfill the goal of precision medicine. Cofactor’s ImmunoPrism platform reveals each patient's immune profile using RNA from FFPE solid tumor samples. With its machine learning-based Predictive Immune Modeling approach, the company combines key immune signals into a predictive biomarker. These biomarkers are then utilized in Cofactor’s OncoPrism test, which assists clinicians in determining the probability of a patient's positive response to immunotherapy.

Cofactor is now leveraging samples and data from one of the largest biobanks in the U.S. to expedite the development of the OncoPrism test for 11 types of cancer. The company's goal is to establish biomarkers for cancers under study in the national PREDAPT (Predicting Immunotherapy Efficacy From Analysis of Pre-treatment Tumor Biopsies) clinical trial. The initial focus is on examining OncoPrism in head, neck, and lung cancers and will soon broaden to include nine other approved indications such as triple-negative breast, cervical, colorectal, esophageal, gastric, kidney, liver, and urothelial cancers. Preliminary results for the head and neck cancer biomarker indicate that Cofactor’s method is twice as accurate as the PD-L1 biomarker in identifying the subgroup of patients who respond positively to immune checkpoint inhibitors.

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