Potential for more personalized medical treatment illustrated by breast cancer study

Using machine learning techniques, researchers have found a subset of breast cancer patients that have much worse outcomes than is typical -- "the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group." This is pretty remarkable, although the confidence interval is pretty huge:

Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely
consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5)

The key thing here is that this is a subset of a subset -- the kind of pattern difficult to extract without statistical methods. The authors go on to say:

The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. ... Our work presented here may contribute a crucial step towards rational design of personalized treatment."

The study was publised in Cancer Research (A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res, 2005;65(10):4059-4066).