How does COXEN work?

Using statistical techniques, we examine gene expression intensity values for the NCI-60 cancer cell lines, for which drug response information, in the form of GI50 data, is available, to determine which genes are associated with drug sensitivity. Next, we determine which of these genes are co-expressed in a set of cancer samples, generating a probability of response. More detailed information is available in the article by Lee et al., "A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery", located at PNAS.

The COXEN algorithm is composed of six distinct steps. The end result is the what we term the "COXEN score", which reflects the predicted sensitivity of a particlar cell line or human tumor to the specific drug being evaluated by the algorithm. Generically, the steps for prediction of a drug's activity in cells belonging to some set 2 on the basis of its activity pattern in different cells of some set 1 are as follows: 

Step 1. Experimentally determine the drug's pattern of activity in cells of set 1.
Step 2. Experimentally measure molecular characteristics of the cells in set 1.
Step 3. Select a subset of these molecular characteristics that most accurately
        predicts the drug's activity in cell set 1 ("chemosensitivity signature"
        selection).
Step 4. Experimentally measure the same molecular characteristics of the cells in
        set 2.
Step 5. Among the molecular characteristics selected in step 3, identify a subset that
        allows a strong pattern of coexpression extrapolation between cell sets 1
        and 2.
Step 6. Use a multivariate algorithm to predict the drug's activity in set 2 cells on
        the basis of the drug's activity pattern in set 1 and the molecular
        characteristics of set 2 selected in step 5.  The output of the multivariate
        analysis is a COXEN score.