Algorithm Helps Detect Cancer Genes

The algorithm has been applied to map tumor-suppressor genes involved in lung cancer.

Research using gene-chips found that chromosomal segments, when deleted in single or both copies of genomes in a group of cancer patients, point to locations of tumor-suppressor genes implicated in the cancer. Researchers at New York University's Courant Institute of Mathematical Sciences (New York, NY, USA) focused on automatic methods for reliable detection of these genes, their locations, and boundaries. The algorithm they developed uses data from Affymetrix (Santa Clara, CA, USA) gene-chips to pinpoint genes involved in cancer, even when the genomes have other unrelated deletions.

In order to validate their algorithm, the researchers produced a high fidelity in silico model of cancer, and checked how well they could detect the right genes, as they modified various parameters of the model. They applied the algorithm to patient data and were able to discover many genes already known in the literature, in addition to several others that were statistically significant. The algorithm may be suitable for the detection of oncogenes, which when mutated or overexpressed can promote cancer growth.

The study was conducted by Professor Bud Mishra of NYU's Courant Institute and School of Medicine (New York, NY, USA) and colleagues. The findings are to be reported in the July 2006 issue of the American Journal of Human Genetics.

Members of the New York University group, Dr. Salvatore Paxia, and Dr. Thomas Anantharaman are in the process of creating a simpler interface for the software, providing interoperability across many different chip technologies, so that it can become publicly available.



Related Links:
Courant Institute of Mathematical Sciences, NYU
School of Medicine NYU