Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics

Abstract

Compared with normal cells, tumor cells have undergone an array of genetic and epigenetic alterations. Often, these changes underlie cancer development, progression, and drug resistance, so the utility of model systems rests on their ability to recapitulate the genomic aberrations observed in primary tumors. Tumor-derived cell lines have long been used to study the underlying biologic processes in cancer, as well as screening platforms for discovering and evaluating the efficacy of anticancer therapeutics. Multiple -omic measurements across more than a thousand cancer cell lines have been produced following advances in high-throughput technologies and multigroup collaborative projects. These data complement the large, international cancer genomic sequencing efforts to characterize patient tumors, such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Given the scope and scale of data that have been generated, researchers are now in a position to evaluate the similarities and differences that exist in genomic features between cell lines and patient samples. As pharmacogenomics models, cell lines offer the advantages of being easily grown, relatively inexpensive, and amenable to high-throughput testing of therapeutic agents. Data generated from cell lines can then be used to link cellular drug response to genomic features, where the ultimate goal is to build predictive signatures of patient outcome. This review highlights the recent work that has compared -omic profiles of cell lines with primary tumors, and discusses the advantages and disadvantages of cancer cell lines as pharmacogenomic models of anticancer therapies.

Figure 1

Breast and bladder cancer cell lines cluster into basal and luminal subtypes. Breast (6) or bladder (35) cancer gene expression data were log-transformed, median centered, then hierarchically clustered using Euclidean distance according to the genes present in the patient-derived (A and B) BASE47 bladder cancer signature or the (C and D) PAM50 breast cancer signature.

Figure 2

Process of using in vitro genomics for clinical treatment selection. Multiple prediction algorithms have been utilized to derive molecular features predictive of pharmacologic response of cells in vitro. After a tumor is biopsied and its genomic features are profiled, in vitro derived predictive signatures may be used to determine the best therapy for the individual’s tumor.