Predicting Clinical Outcomes
Predicting patient outcomes and recurrence rates is an inexact science. Based on the type of cancer, clinicians depend on disease staging based tumor size and spread, and on clinical subtype identification to provide insight into a cancer’s aggressiveness and likelihood of return. The disease stage is often the single most important factor in treatment selection.

Numerous publications have shown how Affymetrix microarray technology has uncovered complex molecular characteristics of cancers that influence survival and recurrance, and in doing so has opened the door for better prognostic tests.

Wright, G. et al, PNAS 2003 demonstrated a gene expression signature that identified molecularly distinct subgroups of Diffuse Large B Cell Lymphoma that correlated with survival.