Global computational alignment of tumor and cell line transcriptional profiles

Abstract

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.

Abstract

The determination of whether cancer cell lines recapitulate the molecular features of corresponding patient tumours remains essential for the selection of appropriate cell line models for preclinical studies. The method developed here, Celligner, integrates cancer cell line and tumour RNA-seq datasets and reveals large differences in their concordance across cell lines and cancer types.