A single nucleotide polymorphism genotyping platform for the authentication of patient derived xenografts

Jad El-Hoss, Duohui Jing, Kathryn Evans, Cara Toscan, Jinhan Xie, Hyunjoo Lee, Renea A. Taylor, Mitchell G. Lawrence, Gail P. Risbridger, Karen L. MacKenzie, Rosemary Sutton, Richard B. Lock

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

Patient derived xenografts (PDXs) have become a vital, frequently used, component of anti-cancer drug development. PDXs can be serially passaged in vivo for years, and shared across laboratories. As a consequence, the potential for mis-identification and cross-contamination is possible, yet authentication of PDXs appears limited. We present a PDX Authentication System (PAS), by combining a commercially available OpenArray assay of single nucleotide polymorphisms (SNPs) with in-house R studio programs, to validate PDXs established in individual mice from acute lymphoblastic leukemia biopsies. The PAS is sufficiently robust to identify contamination at levels as low as 3%, similar to the gold standard of short tandem repeat (STR) profiling. We have surveyed a panel of PDXs established from 73 individual leukemia patients, and found that the PAS provided sufficient discriminatory power to identify each xenograft. The identified SNP-discrepant PDXs demonstrated distinct gene expression profiles, indicating a risk of contamination for PDXs at high passage number. The PAS also allows for the authentication of tumor cells with complex karyotypes from solid tumors including prostate cancer and Ewing’s sarcoma. This study highlights the demands of authenticating PDXs for cancer research, and evaluates a reliable authentication platform that utilizes a commercially available and cost-effective system.

Original languageEnglish
Pages (from-to)60475-60490
Number of pages16
JournalOncotarget
Volume7
Issue number37
DOIs
Publication statusPublished - 9 Aug 2016

Keywords

  • Authentication
  • OpenArray
  • Patient derived xenografts
  • R studio
  • SNP genotyping

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