Problem
Eight million people die from cancer each year, and 90% of these deaths happen in cases where the cancer has spread throughout the body from the initial tumor site.1 In the early stages of cancer, tumor cells can shed from the primary mass and enter the bloodstream. Clinicians can glean important information about the tumor from these circulating tumor cells (CTCs). CTC analysis requires only a blood draw, and most forms of analysis can be done rapidly on automated machines. The wait for diagnosis from biopsy, which can take weeks to months, is harrowing for a patient and delays treatment. Widespread adoption of CTC analysis would drastically improve cancer care.
Unfortunately, only a few CTC detection methods have been approved for routine clinical use. The problem is that in a single blood draw, only a few CTCs exist among millions of blood cells.2 Furthermore, most analytical techniques rely on a specific marker of cancer, while many subtypes exist that express different markers. For routine clinical adoption of CTC screening, a system is needed with a streamlined workflow that can reliably detect a variety of cancer subtypes from a few circulating tumor cells.
Solution
In collaboration with Ruth Katz at MD Anderson Cancer Center, the Eberlin Lab at The University of Texas at Austin is developing a clinical tool for routine clinical analysis of CTCs. Starting with a blood draw, the first step is to use methods routine to any clinical lab to remove the plasma and red blood cells. The remaining cells are then placed on a glass slide so that they can be analyzed by a mass spectrometer which provides a readout almost immediately. A machine learning algorithm helps identify metabolic signatures that are specific to certain cancer types. The entire analysis can be done in a short amount of time in a hospital lab setting. By focusing on metabolic signatures instead of cell surface markers or specific genetic mutations, this approach can catch a variety of cancer subtypes.
The combination of highly sensitive mass spectrometry and machine learning makes it possible to quickly identify the presence of rare CTCs without the need for extensive isolation and purification techniques. This new system has the potential to drastically improve the standard of care for cancer treatment by enabling minimally invasive screening and earlier diagnosis.
1. Cristofanilli M, Budd GT, Ellis MJ, et al: Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004;351:781-791
2. Podotar PD., Lotokey, NK., J Cancer Metastasis Treat 2015;1:44-56. 10.4103/2394-4722.158803
