Improving Disease Diagnosis
Cancer diagnosis and treatment decisions have relied heavily upon tissue morphology/pathology and traditional imaging techniques. Whole-genome analysis of gene expression in tissue and blood has provided a more granular tool to identify entirely new subtypes of disease that have been missed through traditional diagnostic methods.

Studies on medulloblastoma, prostate cancer, breast cancer, lung cancer, colon cancer, renal cell carcinoma, and diffuse large B-cell lymphoma are just a few examples of cancers in which established microarray classification systems have been developed using gene expression signatures, often offering important prognostic indications for cancer outcome and recurrence, as well as patient response to treatment. Whole genome genotyping holds similar promise in identifying gene variants that distinguish cancer subtypes.

Many of these signatures are being validated for future clinical use, and the Powered by Affymetrix™ program is providing Molecular Diagnostics partners a route for these and many other emerging signatures to be commercialized on Affymetrix? diagnostics platform.

Expression profiling by Armstrong et al. classified patient samples to the appropriate type of cancer (acute lymphoblastic leukemia, mixed lineage leukemia or acute myelogenous leukemia) with 95% accuracy. Acute lymphoblastic leukemia patients with a MLL gene translocation have a particularly poor prognosis.