Those tumors include neuroblastoma, rhabdomyosarcoma, non-Hodgkin's lymphoma and the Ewing family of tumors. The first step involves microarrays, which are about the size of a microscope slide and contain specific fragments of specially chosen DNA, the genetic building block. An active gene sends out a chemical signal called RNA, which carries a particular sequence of building blocks that tell what gene it came from. A tissue sample from the cancer is spread on the microarray, and each RNA string then attaches to a waiting DNA segment designed to attract it. By detecting how many strings end up at each site on the chip, instruments can tell how active the corresponding gene had been in the tumor.
Many strings signify a very active gene.
No strings at a particular site means the corresponding gene had been turned off. This information is then fed into a computer program called an artificial neural network, which has learned which gene segments are most active for differing types of cancer. The data goes through a training phase, and the computer tries to learn the features of the data that allow it to make a classification. He wouldn't speculate how soon. In addition, Meltzer noted, the process allows scientists to learn gene profiles of the various types of cancer.