A framework to select clinically relevant cancer cell lines for investigation by establishing their molecular similarity with primary human cancers
Cette étude présente une méthode permettant d'évaluer, sur la base de profils d'expression de gènes, le degré de similarité entre des lignées cellulaires et des tumeurs humaines et, ainsi, de faciliter la traduction clinique de travaux menés in vitro au laboratoire
Experimental work using human cancer cell lines often does not translate to the clinic. We posit this is because some cells undergo changes in vitro that no longer make them representative of human tumors. Here we describe a novel alignment method named SRCCM (Spearman's Rank Correlation Classification Method) that measures similarity between cancer cell lines and human tumors via gene expression profiles, for the purpose of selecting lines that are biologically relevant. To demonstrate utility, we used SRCCM to assess similarity of 36 bladder cancer lines with 10 epithelial human tumor types (N=1630 samples) and with bladder tumors of different stages and grades (N=144 samples). While 34/36 lines aligned to bladder tumors rather than other histologies, only 16/28 had SRCCM assigned grades identical to that of their original source tumors. To evaluate the clinical relevance of this approach, we demonstrate that gene expression profiles of aligned cell lines stratify survival in an independent cohort of 87 bladder patients (HR = 3.41, logrank p = 0.0077), while unaligned cell lines using original tumor grades did not. We repeated this process on 22 colorectal cell lines and found that gene expression profiles of 17 lines aligning to colorectal tumors and selected based on their similarity with 55 human tumors stratified survival in an independent cohort of 177 colorectal cancer patients (HR = 2.35, logrank p = 0.0019). By selecting cell lines that reflect human tumors, our technique promises to improve the clinical translation of laboratory investigations in cancer.
Cancer Research , résumé, 2011