November Edition 2020

Cofactor Genomics: Achieve True Predictive Medicine


The Human Genome Project is without question one of humanity’s greatest accomplishments. The technologies and tools that resulted from this effort have changed how we approach medicine. Having worked on the project, Jarret Glasscock, a geneticist and computational biologist, saw the limitations of DNA and the potential advantages of analyzing RNA. With this concept in mind, in 2009 he founded Cofactor Genomics to leverage the power of RNA to bridge the Precision Medicine Gap that exists between patients and the therapies that will benefit them.

Today, Cofactor Genomics is the leader in RNA. They work in partnership with medical centers, drug developers, and clinical research organizations to offer the most advanced machine-learning tools, deep databases, and molecular reagents brought together in their Predictive Immune Modeling platform. Cofactor’s technology enables expedited development of multidimensional, predictive diagnostics that empower physicians to improve treatment decisions.

Jarret Glasscock discussed the developments at Cofactor Genomics in an interview. Here are the excerpts from the interview:

Q. What is the Precision Medicine Gap, and how is it impacting healthcare? Specifically, what is its impact on the patients, doctors, and payers?

The Precision Medicine Gap arose from the industry’s massive investment in therapeutics, many of which are revolutionary, but they only work in a subset of patients. Today, this gap exists because of the disparity between the bolus of therapies and the limited number of patients who benefit.

Matchmaking technologies that are capable of identifying patient populations who will respond to each therapy class ahead of treatment are paramount. Without these technologies – also known as predictive diagnostics – we will continue to treat patients with medicines that they don’t respond to, and that may cause adverse events. This will result in poorer outcomes for patients, disappointment for treating physicians, and ultimately, additional unnecessary expense for payers.

Q. How did you land on tackling this specific problem?

During the early years of our company, we worked closely with the world’s largest pharmaceutical companies to provide next-generation sequencing tools that streamlined biomarker discovery and diagnostic development. We recognized RNA’s value to precision medicine and what was needed. There was value in building databases that could be leveraged clinically, but we needed to move beyond big data generation into meaningful models of disease. With the advent of immunotherapies, we found our niche by using RNA data to build models of immune response with Predictive Immune Modeling, the underlying technology that powers our diagnostic development. Measuring immune response at the site of the solid tumor is essential to predicting immunotherapy response rates, and our technology is the first to bring these signals together into one clinical decision.

Q. How is what you’re doing different than other approaches that came before you?

Historically clinical tests have relied on single-analyte biomarkers for treatment decisions. Unfortunately, this single-analyte world has failed us, and is not enabling us to close the Precision Medicine Gap. The discipline of Predictive Immune Modeling is moving medicine from this single-analyte world to the new world of multidimensional models of disease - specifically using dynamic RNA data and machine-learning tools.

The second difference is that many diagnostic approaches today have focussed on the tumor attributes of the cancer cell itself. For example, measuring a panel of mutations and other genomic aberrations present in the cancerous cells. However, this excludes a key facet of cancer – the role of our immune system. We are part of a new generation of diagnostics, focused on characterizing immune response. This new generation of tools is inspired by the well-established connection between immune activation and therapy response, particularly in therapies such as immunotherapies.

Ultimately, these differences result in a multidimensional immune-based approach that is more comprehensive and yet at the same time more sensitive. With this approach, we are achieving key milestones that the industry has been striving for in the era of genomics-driven diagnostics.

Q. What are the data assets that underpin your technology?

While the industry remains focused on “big data”, Cofactor leveraged the computational expertise to restructure RNA data into meaningful models. Our database of Health Expression Models enables us to move out of the noise and detect important biological signals with better sensitivity and specificity. Built using machine-learning, the models not only capture the presence or absence or RNA signals, but also the dynamic expression levels. Each model in our assay captures subtle nuances between the immune cells known to be important in our body’s fight against cancer. These models represent a significant investment made by Cofactor, necessary to build a diagnostic tool that will make an impact in a clinical setting. At the end of the day, technology’s role is to make the complex simple. This is what we are achieving with the models we have built.

Q. How many people in the US does your diagnostic have the potential to impact?

Our diagnostic is currently focussed on informing treatment in the types of cancers being prescribed immune checkpoint inhibitors, a particular class of immunotherapy. These cancers currently account for 50% of yearly diagnosed cancers in the US, or roughly 1 million cancers per year. When these therapies work, they work very well. However, less than 25% of patients respond to these therapies, so when would-be non-responders are put on these therapies, they are wasting precious time. Time that many patients don’t have. Our team aims to change that trajectory and improve the treatment path for 1 million patients or more per year.

Q. How do you see healthcare changing in the next 5 years? What is Cofactor’s role in that vision?

Patients and their treating physicians will continue to expect medicine to become more precise. This advancement is not possible without new, innovative tools in the diagnostic space. Ultimately, there is no precision medicine without precision diagnostics. Cofactor is eager to bridge the Precision Medicine Gap that currently exists between the therapy options and the patients who will respond.

The RNA Expert

Jarret Glasscock, PhD - Founder and CEO: Jarret has contributed extensively to the field of RNA-based genomic research. Prior to founding Cofactor Genomics, he was faculty in the Department of Genetics at Washington University in St. Louis and was part of The Genome Institute. At Washington University, he led the Institute’s Computational Biology Group. His work has been covered by Genetic Engineering News (GEN), Tech Crunch, Wired Magazine, and more.

"While the industry remains focused on “big data”, Cofactor has leveraged their computational expertise to restructure RNA data into meaningful models.”