The clinical relevance of cancer cell lines

Abstract

Although advances in genomics during the last decade have opened new avenues for translational research and allowed the direct evaluation of clinical samples, there is still a need for reliable preclinical models to test therapeutic strategies. Human cancer-derived cell lines are the most widely used models to study the biology of cancer and to test hypotheses to improve the efficacy of cancer treatment. Since the development of the first cancer cell line, the clinical relevance of these models has been continuously questioned. Based upon recent studies that have fueled the debate, we review the major events in the development of the in vitro models and the emergence of new technologies that have revealed important issues and limitations concerning human cancer cell lines as models. All cancer cell lines do not have equal value as tumor models. Some have been successful, whereas others have failed. However, the success stories should not obscure the growing body of data that motivates us to develop new in vitro preclinical models that would substantially increase the success rate of new in vitro-assessed cancer treatments.

Figure 1.

Correlation of gene expression data from three distinct platforms. Expression profiles for ABCB1 across all 60 cell lines were compared between: SYBR Green and microarray (A); TaqMan low-density array (TLDA) and microarray (B); SYBR Green and TLDA (C); SYBR Green and microarray (D); TLDA and microarray (E); SYBR Green and TLDA (F). The data show that TLDA provides more sensitivity, yielding a larger dynamic range of measurement. The coefficient of correlation is given for each comparison. qRT-PCR = quantitivate reverse transcription–polymerase chain reaction. [Reprinted from (30), by permission.]

Figure 2.

Hierarchical clustering using the average linkage algorithm and 1 − Pearson correlation as the distance measure of the ovarian cancer samples analyzed. A) The 380 multidrug resistance–linked gene expression profile (measured using TaqMan low-density array) of ovarian cancer models (in vitro and in vivo) is strikingly different from that of specimens of untreated ovarian primary serous carcinoma taken from 80 patients as well as 32 effusion samples originating from primary ovarian serous carcinoma. The X-axis shows clusters of samples. Red = primary ovarian serous carcinoma; magenta = effusion samples originating from primary ovarian serous carcinoma; green = normal ovarian tissue; blue = in vitro models of ovarian cancer, including xenograft models of ovarian cancer, ovarian cancer cell lines of the National Cancer Institute 60 (NCI-60) panel, and cisplatin-resistant cell lines. The Y-axis shows gene clustering. B) When adding the eight additional cancer types of the NCI-60 panel to the heatmap presented in panel A, the striking observation is made that all the cell lines either grown in vitro or in vivo bear more resemblance to each other, regardless of the tissue of origin, than to the clinical samples that they are supposed to model. Along the X-axis: red = primary ovarian serous carcinoma; magenta = effusion samples originating from primary ovarian serous carcinoma; green = normal ovarian tissue; blue = in vitro models of ovarian cancer; black = cancer cell lines of the eight additional cancer types of the NCI-60 panel. The Y-axis shows gene clustering. [Reprinted from (38).]

Figure 3.

Comparison of mRNA expression profiles in cell lines and primary tumors of six different tissue types (CNS = central nervous system). We selected 255 genes with the highest variance in expression (variance >1) across all samples (primary tumors and cell lines). Then we computed the fold difference for each tissue separately in primary tumors and cell lines as the average fold difference between samples from that tissue and a random set of samples from all other tissues (n = 5 from each tissue). The correlation was calculated using the average fold change for the primary tumors and the average fold change for the cell lines. For each tumor type, the log fold change of the 5000 most variable genes was calculated between that tumor type and all others. Pearson correlations between tumor type fold changes from primary tumors and cell lines are shown as a heat map.