A major challenge of cancer treatment is the selection of chemotherapies that maximize efficacy and minimize toxicity for a given patient. Assays for cell surface markers, e.g., using immunohistochemistry (IHC), have provided means for dividing certain cancers into subclasses. For example, one factor considered in prognosis and treatment decisions for breast cancer is the presence or absence of the estrogen receptor (ER). ER-positive breast cancers typically respond much more readily to hormonal therapies such as tamoxifen, which acts as an anti-estrogen in breast tissue, than ER-negative cancers. Though useful, these analyses only in part predict the clinical behavior of breast cancers. There is phenotypic diversity present in cancers that current diagnostic tools fail to detect. As a consequence, there is still much controversy over how to stratify patients amongst potential treatments in order to optimize outcome (e.g., for breast cancer see “NIH Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1-3, 2000”, J. Nat. Cancer Inst. Monographs, 30:5-15, 2001 and Di Leo et al., Int. J. Clin. Oncol. 7:245-253, 2002). In particular, there is currently no tool for predicting a patient's likely response to treatment with paclitaxel, a chemotherapeutic with particularly adverse side-effects. There clearly exists a need for improved methods and reagents for classifying cancers and thereby selecting therapeutic regimens that maximize efficacy and minimize toxicity for a given patient.