Evaluating the efficacy of multiple myeloma cell lines as models for patient tumors via transcriptomic correlation analysis

Leukemia. 2020 Oct;34(10):2754-2765. doi: 10.1038/s41375-020-0785-1. Epub 2020 Mar 2.


Multiple myeloma (MM) cell lines are routinely used to model the disease. However, a long-standing question is how well these cell lines truly represent tumor cells in patients. Here, we employ a recently described method of transcriptional correlation profiling to compare similarity of 66 MM cell lines to 779 newly diagnosed MM patient tumors. We found that individual MM lines differ significantly with respect to patient tumor representation, with median R ranging from 0.35 to 0.54. ANBL-6 was the "best" line, markedly exceeding all others (p < 2.2e-16). Notably, some widely used cell lines (RPMI-8226, U-266) scored poorly in our patient similarity ranking (48 and 52 of 66, respectively). Lines cultured with interleukin-6 showed significantly improved correlations with patient tumor (p = 9.5e-4). When common MM genomic features were matched between cell lines and patients, only t(4;14) and t(14;16) led to increased transcriptional correlation. To demonstrate the utility of our top-ranked line for preclinical studies, we showed that intravenously implanted ANBL-6 proliferates in hematopoietic organs in immunocompromised mice. Overall, our large-scale quantitative correlation analysis, utilizing emerging datasets, provides a resource informing the MM community of cell lines that may be most reliable for modeling patient disease while also elucidating biological differences between cell lines and tumors.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Line, Tumor
  • Disease Models, Animal
  • Gene Expression Profiling*
  • Gene Expression Regulation, Neoplastic
  • Genomics / methods
  • Humans
  • Immunophenotyping
  • Mice
  • Multiple Myeloma / genetics*
  • Multiple Myeloma / metabolism
  • Multiple Myeloma / mortality
  • Mutation
  • Prognosis
  • Reproducibility of Results
  • Transcriptome*