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Cancer Res. 2009 Nov 1;69(21):8302-9. doi: 10.1158/0008-5472.CAN-09-0798. Epub 2009 Oct 20.

Concordant gene expression signatures predict clinical outcomes of cancer patients undergoing systemic therapy.

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Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22908, USA.


Conventional development of multivariate gene expression models (GEM) predicting therapeutic response of cancer patients is based on analysis of patients treated with specific regimens, which limits generalization to different or novel drug combinations. We overcome this limitation by developing GEMs based on in vitro drug sensitivities and microarray analyses of the NCI-60 cancer cell line panel. These GEMs were evaluated in blind fashion as predictors of tumor response and/or patient survival in seven independent cohorts of patients with breast (n = 275), bladder (n = 59), and ovarian (n= 143) cancer treated with multiagent chemotherapy, of which 233 patients were from prospectively enrolled clinical trials. In all studies, GEMs effectively stratified tumor response and patient survival independent of established clinical and pathologic tumor variables. In bladder cancer patients treated with neoadjuvant methotrexate, vinblastine, Adriamycin (doxorubicin), and cisplatin, the 3-year overall survival for those with favorable GEM scores was 81% versus 33% for those with less favorable scores (P = 0.002). GEMs for breast cancer patients treated with 5-fluorouracil, Adriamycin (doxorubicin), and cyclophosphamide and ovarian cancer patients treated with platinum-containing regimens also stratified patient survival [5-year overall survival 100% versus 74% (P = 0.05) and 3-year overall survival 68% versus 43% (P = 0.008), respectively]. Importantly, clinical prediction using our in vitro GEM was superior to that of conventionally derived GEMs. We show a facile yet effective approach to GEM derivation that identifies patients most likely to benefit from selected multiagent therapy. Use of such in vitro-based GEMs may provide a robust and generalizable approach to personalized cancer therapy.

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