PURPOSE: Standard treatment for advanced non-small-cell lung cancer (NSCLC) includes the use of a platinum-based chemotherapy regimen. However, response rates are highly variable. Newer agents, such as pemetrexed, have shown significant activity as second-line therapy and are currently being evaluated in the front-line setting. We utilized a genomic strategy to develop signatures predictive of chemotherapeutic response to both cisplatin and pemetrexed to provide a rational approach to effective individualized medicine. METHODS: Using in vitro drug sensitivity data, coupled with microarray data, we developed gene expression signatures predicting sensitivity to cisplatin and pemetrexed. Signatures were validated with response data from 32 independent ovarian and lung cancer cell lines as well as 59 samples from patients previously treated with cisplatin. RESULTS: Genomic-derived signatures of cisplatin and pemetrexed sensitivity were shown to accurately predict sensitivity in vitro and, in the case of cisplatin, to predict treatment response in patients treated with cisplatin. The accuracy of the cisplatin predictor, based on available clinical data, was 83.1% (sensitivity, 100%; specificity 57%; positive predictive value, 78%; negative predictive value, 100%). Interestingly, an inverse correlation was seen between in vitro cisplatin and pemetrexed sensitivity, and importantly, between the likelihood of cisplatin and pemetrexed response in patients. CONCLUSION: The use of genomic predictors of response to cisplatin and pemetrexed can be incorporated into strategies to optimize therapy for advanced solid tumors.
PURPOSE: Standard treatment for advanced non-small-cell lung cancer (NSCLC) includes the use of a platinum-based chemotherapy regimen. However, response rates are highly variable. Newer agents, such as pemetrexed, have shown significant activity as second-line therapy and are currently being evaluated in the front-line setting. We utilized a genomic strategy to develop signatures predictive of chemotherapeutic response to both cisplatin and pemetrexed to provide a rational approach to effective individualized medicine. METHODS: Using in vitro drug sensitivity data, coupled with microarray data, we developed gene expression signatures predicting sensitivity to cisplatin and pemetrexed. Signatures were validated with response data from 32 independent ovarian and lung cancer cell lines as well as 59 samples from patients previously treated with cisplatin. RESULTS: Genomic-derived signatures of cisplatin and pemetrexed sensitivity were shown to accurately predict sensitivity in vitro and, in the case of cisplatin, to predict treatment response in patients treated with cisplatin. The accuracy of the cisplatin predictor, based on available clinical data, was 83.1% (sensitivity, 100%; specificity 57%; positive predictive value, 78%; negative predictive value, 100%). Interestingly, an inverse correlation was seen between in vitro cisplatin and pemetrexed sensitivity, and importantly, between the likelihood of cisplatin and pemetrexed response in patients. CONCLUSION: The use of genomic predictors of response to cisplatin and pemetrexed can be incorporated into strategies to optimize therapy for advanced solid tumors.
Authors: Donald A Richards; David Loesch; Svetislava J Vukelja; Hillary Wu; William J Hyman; Jeffery Nieves; Yunfei Wang; Simin Hu; Oluwatoyin O Shonukan; Datchen F Tai Journal: Invest New Drugs Date: 2010-03-30 Impact factor: 3.850
Authors: David S Hsu; Chaitanya R Acharya; Bala S Balakumaran; Richard F Riedel; Mickey K Kim; Marvaretta Stevenson; Sascha Tuchman; Sayan Mukherjee; William Barry; Holly K Dressman; Joseph R Nevins; Scott Powers; David Mu; Anil Potti Journal: Proc Natl Acad Sci U S A Date: 2009-03-11 Impact factor: 11.205
Authors: John H Farley; Chunqiao Tian; G Scott Rose; Carol L Brown; Michael Birrer; John I Risinger; J Tate Thigpen; Gini F Fleming; Holly H Gallion; G Larry Maxwell Journal: Cancer Date: 2010-01-15 Impact factor: 6.860
Authors: Meng Li; Curt Balch; John S Montgomery; Mikyoung Jeong; Jae Hoon Chung; Pearlly Yan; Tim H M Huang; Sun Kim; Kenneth P Nephew Journal: BMC Med Genomics Date: 2009-06-08 Impact factor: 3.063