Pancreatic cancer is one of the deadliest cancers. For the few patients diagnosed before the cancer spreads, the 5-year survival rate is almost 60 percent; in nearly every case, the survival rate is less than five percent. In an era where healthcare is so advanced, it’s an unfair statistic. That’s why even the slightest breakthroughs in the area provide hope to millions of patients worldwide. A team led by Dr. Andrew Lowy, clinical director for Cancer Surgery at UC San Diego School of Medicine, is set out to mitigate this predicament by developing a

novel screening platform to detect early stage cancer.

Lowy and his colleague recently published their technique that uncovered more than 95 percent of early pancreatic cancers. The new technique revolves around tapping into the intercellular communication network. They purified extracellular vesicles (EVs) the tiny, bubble-like blobs that mediate cell-to-cell communication from the blood of patients with early pancreatic, ovarian, and bladder cancers. Subsequently, they performed an analysis of the protein composition of the samples. Lowry and his team developed a machine-learning algorithm to detect a small set of EV proteins by comparing the samples from cancer and control patients. These proteins can be used to detect early-stage pancreatic, ovarian, and bladder cancers. They proclaim that their algorithm successfully detected 95.5 percent of stage-1 pancreatic cancers. The program also diagnosed 73.1 percent of stage-1 ovarian cancers and 43.8 percent of stage-1 bladder cancers. The method is potentially a ‘life changing’ technology for early cancer detection. Do let us know your thoughts.