Grant: Project Grant

CRC is the 2nd most deadly cancer in Canada, many patients develop metastasis; of those, > 50% develop liver metastasis (CRCLM) with 70% mortality. The commonly used pre-surgery therapy appears harmful to 20-40% of patients, yet which patients cannot be determined beforehand, and they all receive the same therapy. Extracellular vesicles (EVs) are nanometer-sized particles secreted by cells. EVs contain multiple molecules which collectively form a molecular 'fingerprint' that can be used to trace back an EV to the cell from which it originated. EVs are emerging as a new class of biomarker because they are secreted in great abundance by cancer cells along with a 'cancer-cell fingerprint', readily travel into blood, and could thus be measured following a simple blood draw. However, EVs must be measured individually to reveal the cancer fingerprint, which was not possible until recently. We invented and developed a new EV analysis technology called SEAM (single EV analysis microarray) for measuring the molecular cargo (that constitute the fingerprint) of single EVs with unprecedented resolution. Here we propose to use SEAM to screen single EVs present in the blood of colorectal cancer liver metastasis (CRCLM) patients, and uncover the EVs carrying the fingerprint of CRCLM cancer. We call these EVs classifier EVs because they could be used to distinguish between different types of CRCLM and classify patients accordingly. With SEAM and computational AI analysis we propose to identify classifier EVs and patients that might be harmed by current therapies, and safe them from it. Secondly, we propose to follow patients throughout their treatment journey and analyze their blood continuously, and identify classifier EVs that reflect patient response to therapy, and prognosticate outcome. The classifier EVs we identify could be used as diagnostics, guide therapeutic choices, advance our understanding of CRCLM, and help discover new therapies.