High-Throughput Molecular Cancer Cell Line Characterization Using Digital Multiplex Ligation-Dependent Probe Amplification for Improved Standardization of in Vitro Research.
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Embargo End Date
ICR Authors
Authors
Menezes, K
Atanesyan, L
Sherborne, AL
Steenkamer, M
Clemens, I
Savola, S
Kaiser, MF
Atanesyan, L
Sherborne, AL
Steenkamer, M
Clemens, I
Savola, S
Kaiser, MF
Document Type
Journal Article
Date
2020-09-01
Date Accepted
2020-06-08
Abstract
Tumor cell lines are widely used for cancer research, but challenges regarding quality control of cell line identity, cross contamination, and tumor somatic molecular stability remain, demanding novel approaches beyond conventional short tandem repeat profiling. A total of 21 commonly used multiple myeloma cell lines obtained from public repositories were analyzed by digital multiplex ligation-dependent probe amplification (digitalMLPA) to characterize germline single-nucleotide polymorphisms, insertions/deletions, and somatic copy number aberrations (CNAs). Using generated profiles and an in-house developed analytical pipeline, blinded experiments were performed to determine capability of digitalMLPA to predict cell line identity and potential spike-in DNA contamination in 41 anonymized cell line samples. The dominant cell line was correctly identified in all cases, and cross contamination was correctly detected in 33 of 37 samples with spike-in DNA; there were no false-positive predictions. The four samples in which spike in was not detected all carried low levels of contamination (1%), whereas levels of contamination ≥5% were correctly identified in all cases. Unsupervised clustering of CNA profiles identified shared commonalities that correlated with initiating Ig heavy locus translocation events. Longitudinal CNA assessment of nine cell lines revealed changes under standard culturing conditions not detected by insertion/deletion profiling alone. Results suggest that digitalMLPA can be utilized as a high-throughput tool for advanced quality assurance for in vitro cancer research.
Citation
The Journal of molecular diagnostics : JMD, 2020, 22 (9), pp. 1179 - 1188
Source Title
Publisher
ELSEVIER SCIENCE INC
ISSN
1525-1578
eISSN
1943-7811
Collections
Research Team
Myeloma Group