PURPOSE: In an attempt to identify genes that are involved in resistance to SN38, the active metabolite of irinotecan (also known as CPT-11), we carried out DNA microarray profiling of matched HCT116 human colon cancer parental cell lines and SN38-resistant cell lines following treatment with SN38 over time. EXPERIMENTAL DESIGN: Data analysis identified a list of genes that were acutely altered in the parental cells following SN38 treatment as well as constitutively altered in the SN38-resistant cells. RESULTS: Independent validation of 20% of these genes by quantitative reverse transcription-PCR revealed a strong correlation with the microarray results: Pearson's correlation was 0.781 (r(2) = 0.61, P < 0.000001) for those genes that were acutely altered in the parental setting following SN38 treatment and 0.795 (r(2) = 0.63, P < 0.000002) for those genes that were constitutively altered in the SN38-resistant cells. We then assessed the ability of our in vitro-derived gene list to predict clinical response to 5-fluorouracil/irinotecan using pretreatment metastatic biopsies from responding and nonresponding colorectal cancer patients using both unsupervised and supervised approaches. When principal components analysis was used with our in vitro classifier gene list, a good separation between responding and nonresponding patients was obtained, with only one nonresponding and two responding patients separating with the incorrect groups. Supervised class prediction using support vector machines algorithm identified a 16-gene classifier with 75% overall accuracy, 81.8% sensitivity, and 66.6% specificity. CONCLUSIONS: These results suggest that in vitro-derived gene lists can be used to predict clinical response to chemotherapy in colorectal cancer.
PURPOSE: In an attempt to identify genes that are involved in resistance to SN38, the active metabolite of irinotecan (also known as CPT-11), we carried out DNA microarray profiling of matched HCT116 humancolon cancer parental cell lines and SN38-resistant cell lines following treatment with SN38 over time. EXPERIMENTAL DESIGN: Data analysis identified a list of genes that were acutely altered in the parental cells following SN38 treatment as well as constitutively altered in the SN38-resistant cells. RESULTS: Independent validation of 20% of these genes by quantitative reverse transcription-PCR revealed a strong correlation with the microarray results: Pearson's correlation was 0.781 (r(2) = 0.61, P < 0.000001) for those genes that were acutely altered in the parental setting following SN38 treatment and 0.795 (r(2) = 0.63, P < 0.000002) for those genes that were constitutively altered in the SN38-resistant cells. We then assessed the ability of our in vitro-derived gene list to predict clinical response to 5-fluorouracil/irinotecan using pretreatment metastatic biopsies from responding and nonresponding colorectal cancerpatients using both unsupervised and supervised approaches. When principal components analysis was used with our in vitro classifier gene list, a good separation between responding and nonresponding patients was obtained, with only one nonresponding and two responding patients separating with the incorrect groups. Supervised class prediction using support vector machines algorithm identified a 16-gene classifier with 75% overall accuracy, 81.8% sensitivity, and 66.6% specificity. CONCLUSIONS: These results suggest that in vitro-derived gene lists can be used to predict clinical response to chemotherapy in colorectal cancer.
Authors: Wendy L Allen; Richard C Turkington; Leanne Stevenson; Gail Carson; Vicky M Coyle; Suzanne Hector; Philip Dunne; Sandra Van Schaeybroeck; Daniel B Longley; Patrick G Johnston Journal: Mol Cancer Ther Date: 2012-06-04 Impact factor: 6.261
Authors: Sandra Van Schaeybroeck; Wendy L Allen; Richard C Turkington; Patrick G Johnston Journal: Nat Rev Clin Oncol Date: 2011-02-15 Impact factor: 66.675
Authors: Wendy L Allen; Leanne Stevenson; Vicky M Coyle; Puthen V Jithesh; Irina Proutski; Gail Carson; Michael A Gordon; Heinz-Josef D Lenz; Sandra Van Schaeybroeck; Daniel B Longley; Patrick G Johnston Journal: Mol Cancer Ther Date: 2011-10-25 Impact factor: 6.261
Authors: H K Kim; I J Choi; C G Kim; H S Kim; A Oshima; Y Yamada; T Arao; K Nishio; A Michalowski; J E Green Journal: Pharmacogenomics J Date: 2010-12-21 Impact factor: 3.550
Authors: Wendy L Allen; Puthen V Jithesh; Gavin R Oliver; Irina Proutski; Daniel B Longley; Heinz-Josef Lenz; Vitali Proutski; Paul Harkin; Patrick G Johnston Journal: BMC Cancer Date: 2010-12-20 Impact factor: 4.430
Authors: Leanne Stevenson; Wendy L Allen; Richard Turkington; Puthen V Jithesh; Irina Proutski; Gail Stewart; Heinz-Josef Lenz; Sandra Van Schaeybroeck; Daniel B Longley; Patrick G Johnston Journal: Clin Cancer Res Date: 2012-08-02 Impact factor: 12.531