Sketching a Solution
Wright was not alone. Around the time he was beginning his work, the same approach occurred to NIH’s Liotta and U.S. Food and Drug Administration researcher Emanuel Petricoin. Petricoin and Liotta knew that cancer, on the level of the cell, generates a cacophony of changes, both in the tumor tissue and in the normal tissue surrounding it. This complexity appears impenetrable. But the duo thought they could exploit that very complexity to generate a cancer fingerprint from traces of the disease circulating in the blood.
Like Wright, Petricoin and Liotta used a Ciphergen system to generate protein profiles from blood samples. Their early attempts to find cancer patterns failed, though, because they were simply trying to juggle too much information. Then, in June 1999, a solution appeared. Petricoin and his friend Peter Levine, a Maryland lawyer with a background in data analysis, were chatting about the problem over brunch; Levine suggested using pattern recognition algorithms to make sense of the massive amount of data. Levine, who had considered using such algorithms to analyze stock market trends and commodities trading, sketched out the cancer idea on a napkin. “In about five minutes, we both realized this would be a really fascinating approach,” Petricoin recalls.
So they tested it, together with Ben Hitt, a software engineer who borrowed the necessary algorithms from artificial-intelligence theory. In fact, cancer patterns did emerge, and in 2000 Levine and Hitt founded Correlogic Systems to develop blood tests for cancers. In early 2002, the researchers published results in the British medical journal Lancet, showing they could use a specific protein pattern to spot ovarian cancer. Their test correctly identified 50 out of 50 women with cancer and correctly scored negative for 63 out of 66 unaffected women. Later given the name OvaCheck, it promised to be the first blood test accurate enough to be used for general ovarian-cancer screening. By the end of 2002, Correlogic had licensed OvaCheck to two major commercial laboratories and planned a 2004 product launch.
Meanwhile, Wright’s group in Virginia was also pushing ahead. Using a different algorithm, Wright and Eastern Virginia molecular biologist John Semmes showed that a protein pattern could distinguish prostate cancer from a common noncancerous condition, benign prostatic hypertrophy, in 25 out of 30 cases. The PSA test, by contrast, is unable to distinguish the two conditions.
While Wright stresses that the results are preliminary, the technology continues to inch toward commercialization. A large initial trial across many medical centers should finish in about a year; a final validation trial will conclude, if all goes well, in 2006. And Eastern Virginia has already licensed its technology to an undisclosed company for eventual development into a full-blown diagnostic test.