Cofactor Genomics: Personalized Medicine through Predictive Immune Modeling

Personalized medicine has been defined as providing the right drug, to the right patient, at the right time. Today, determining a cancer patient’s treatment plan relies on testing their tumor tissue for biological markers, or biomarkers, using a series of molecular or diagnostic assays. These tests primarily focus on single analyte biomarkers. The prominent example of a single analyte marker is the detection of PD-L1 protein in immunohistochemistry (IHC) diagnostics. “We know that the ability of a PD-L1 to predict response to immunotherapy is quite poor, as single analyte biomarkers capture a small facet of the biology of disease,” states Dr. Natalie LaFranzo, Director of Scientific Projects, Cofactor Genomics. Drug developers and clinicians are looking for a biomarker that can capture more of a patient’s immunological profile, their response to therapy, and potentially, the overall prognosis of the health outcome. Hence, Cofactor Genomics strongly advocates the use of multidimensional biomarkers using high-throughput technology like RNA sequencing. “RNA is a better barometer of health as everything happening in your body leaves a trail of RNA. It codes for inflammatory antibodies in case of Parkinson’s, and it enables mutant DNA replication in cells that lead to cancer. On the other hand, DNA-based diagnostics primarily predict your risk of contracting a disease later in life. So, RNA is the sweet-spot between DNA and the proteins it codes,” explains Natalie.

Leveraging the power of RNA sequencing, the company has built a database of unique gene expression models for key immune cells known to play a role in immune-oncology. The company relies on the molecular and informatics pipelines they have built to generate and deploy these Health Expression Models (HEMs) that interpret complex biological data. The multifaceted nature of these models mitigates the chance that unavoidable molecular noise will result in missing signals that are important. And, the Health Expression Models developed by Cofactor have shown increased sensitivity and specificity in detecting cells in the tumor microenvironment of solid tumors, even while requiring less tumor material. However, this improvement in immune profiling alone does not ensure the mandatory advancement in the predictive power necessary to address the challenge of patient selection and precision medicine, and that is why Cofactor has developed a solution which employs Predictive Immune Modeling.