A genomic strategy to combinatorial therapeutics in solid tumors.

2031 Background: For most advanced solid tumors, the response rate to cytotoxic drugs is generally low, highlighting the importance of identifying those patients most likely to respond, either to single agents or combinations of cytotoxic or targeted therapies. METHODS: We have made use of in vitro drug response data generated on the NCI-60 panel of cancer cell lines, coupled with Affymetrix U133 2.0 plus gene expression data, to develop genomic predictors of chemotherapy sensitivity. These models were then validated in independent cancer cell lines as well as response data from patient treatment studies. RESULTS: Predictive models making use of gene expression data were developed for docetaxel, adriamycin, 5-flourouracil, cyclophosphamide, paclitaxel, and topotecan. These models were shown to accurately predict sensitivity to the drugs in an independent set (n = 30) of cancer cell lines. Importantly, three of the predictors (docetaxel, topotecan, paclitaxel) also accurately (> 80%) predicted response in patient studies. When evaluated in a large collection of human cancers (n = 381), these gene expression signatures of drug response identified patterns of predicted sensitivity suggesting potential opportunities for novel combinations. We also combined the predictions of chemotherapy sensitivity with predictions of pathway deregulation (Bild A, Nature 2005), to develop further opportunities for combination therapy. For instance, this analysis revealed a significant relationship between PI3 kinase pathway deregulation and docetaxel resistance (p = 0.001), and a correlation between docetaxel sensitivity and the activation of the Rb/E2F pathway (p = 0.009). Furthermore, cell lines showing an increased probability of PI3 kinase and Rb/E2F activation were also more likely to respond to a PI3 kinase (LY-294002) inhibitor (p = 0.01) or R-Roscovitine (p = 0.03), a cell cycle inhibitor, respectively. CONCLUSIONS: The development and validation of chemotherapeutic response predictors, together with oncogenic pathway signatures that can guide the use of targeted agents, provides an opportunity to develop effective combinatorial therapeutic strategies geared to the individual patient. No significant financial relationships to disclose.

Duke Scholars

Author Michael John Kelley Medicine, Medical Oncology