Cell Reports: Methods (Jun 2021)
A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles
Abstract
Summary: Patient-derived cell lines are often used in pre-clinical cancer research, but some cell lines are too different from tumors to be good models. Comparison of genomic and expression profiles can guide the choice of pre-clinical models, but typically not all features are equally relevant. We present TumorComparer, a computational method for comparing cellular profiles with higher weights on functional features of interest. In this pan-cancer application, we compare ∼600 cell lines and ∼8,000 tumor samples of 24 cancer types, using weights to emphasize known oncogenic alterations. We characterize the similarity of cell lines and tumors within and across cancers by using multiple datum types and rank cell lines by their inferred quality as representative models. Beyond the assessment of cell lines, the weighted similarity approach is adaptable to patient stratification in clinical trials and personalized medicine. Motivation: Cancer is a genetic disease, typically marked by widespread somatic alterations (e.g., mutations, copy-number alterations, and gene expression changes). However, not all changes are functionally important—few genes can promote oncogenesis (also termed “cancer drivers”), whereas other altered genes have little effect on the phenotype (termed “passengers”). Furthermore, many research questions focus on particular genes and their activity (e.g., specific signaling pathways, drug targets, etc.). This motivates the need for a flexible method of comparing tumors with potential cell line models by using researcher-selected properties. We present TumorComparer, a computational comparison method based on weighted features to allow expert- and knowledge-driven comparison of tumors and experimental models, such as cell lines or organoids. We apply TumorComparer to the comparison of ∼8,000 tumors and ∼600 cell lines across 24 cancer types as an initial application to provide a general, pan-cancer resource based on knowledge of oncogenic alterations gained from The Cancer Genome Atlas program (TCGA). TumorComparer is a generally applicable method suitable for pre-clinical cancer research and personalized medicine applications where sets of samples need to be assessed for similarity.
