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Clinical Determinants of Response to Irinotecan-Based Therapy Derived from Cell Line Models

Authors' Affiliations: ¹ Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland and ² Division of Medical Oncology, University of Southern California/Norris Comprehensive Cancer Center, Keck School of Medicine, Los Angeles, California

Requests for reprints: Patrick G. Johnston, Centre for Cancer Research and Cell Biology, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland, Phone: 44-28-90972764; Fax: 44-28-90972596; E-mail: pjmedschool{at}qub.ac.uk.

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 (r² = 0.61, P < 0.000001) for those genes that were acutely altered in the parental setting following SN38 treatment and 0.795 (r² = 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.