James Hicks, PhD

Applying blood biopsy technology to understand cancer progression and find predictive markers that indicate how cancer will react to treatment.

Impact

Liquid biopsy, a blood test that detects cancer cells and other tumor-associated factors, is a promising, less-invasive alternative to tumor biopsy. In addition to more readily providing information about a patient’s cancer, its minimally invasive nature means several samples can be taken over time. This would allow clinicians to have real-time data on a tumor’s response to therapy, informing treatment decisions and potentially revolutionizing the way cancer is managed. Liquid biopsies are not currently available for widespread clinical applications, but Drs. Hicks and Kuhn and their teams are pushing this new technology to expand the scope of what information can be derived from patient blood and exploring its potential in early detection as well as predicting cancer progression and recurrence.

Progress Thus Far

Using a combination of data science, biotechnology, and mathematical tools, the teams explore an array of circulating cells – not just tumor cells thought to influence cancer evolution and metastatic progression – but also immune cells and other tumor-responsive cells not previously studied by liquid biopsy. Through this work, Drs. Hicks, Kuhn, and their teams dramatically improved the methodology for liquid biopsies, leading to the creation of an ‘Atlas’ of the 16-plus distinct cell types that uniquely populate the blood of people with breast cancer and are not found in healthy individuals. Recently, they have demonstrated the ability to accurately distinguish early-stage and late-stage cancer from each other and from normal blood.

What’s next

Motivated by their recent discovery, they are pursuing further investigations to determine whether a simple blood draw could enhance or even replace mammography in routine screening for breast cancer before any symptoms emerge. Their immediate focus will be to activate clinical studies that aim to evaluate their technology’s ability to detect early-stage breast cancer compared to mammography screening. Concurrently, they will optimize their machine learning methods to identify and quantitate cell types and states through their High Definition Single Cell Assay (HD-SCA) workflow. The HD-SCA identifies rare cells and characterizes the genes and proteins that are present in a single cell. It can therefore explore different phases of a disease at the single-cell level. The team will pursue additional studies on the origin and composition of particles shed by cancer cells into the blood as they are discovered.