A multivariate statistical analysis of the effects of styrene maleic acid encapsulated RL71 in a xenograft model of triple negative breast cancer

Authors

  • Orleans N.K. Martey Univ. Otago
  • Khaled Greish Arabian Gulf University, Manama, Kingdom of Bahrain
  • Paul F. Smith University of Otago
  • Rhonda J. Rosengren Univ. Otago

DOI:

https://doi.org/10.14440/jbm.2019.306

Keywords:

breast tumor, RL71, multivariate statistics, data mining

Abstract

We have previously shown that the curcumin derivative 3,5-bis(3,4,5-trimethoxybenzylidene)-1-methylpiperidine-4-one (RL71), when encapsulated in styrene maleic acid micelles (SMA-RL71), significantly suppressed the growth of MDAMB-231 xenografts by 67%. Univariate statistical analysis showed that pEGFR/EGFR, pAkt/Akt, pmTOR/mTOR and p4EBP1/4EPBP1 were all significantly decreased in tumors from treated mice compared to SMA controls. In this study, multivariate statistical analyses (MVAs) were performed to identify the molecular networks that worked together to drive tumor suppression, with the aim to determine if this analysis could also be used to predict treatment outcome. Linear discriminant analysis correctly predicted, to 100% certainty, mice that received SMA-RL71 treatment. Additionally, results from multiple linear regression showed that the expression of Ki67, PKC-α, PP2AA-α, PP2AA-β and CaD1 networked together to drive tumor growth suppression. Overall, the MVAs provided evidence for a molecular network of signaling proteins that drives tumor suppression in response to SMA-RL71 treatment, which should be explored further in animal studies of cancer.

Author Biography

Paul F. Smith, University of Otago

Professor, Dept. Pharmacology and Toxicology, Sch. Biomed Sci,

University of Otago Medical School, Dunedin, New Zealand

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Published

2019-12-16

How to Cite

1.
Martey ON, Greish K, Smith PF, Rosengren RJ. A multivariate statistical analysis of the effects of styrene maleic acid encapsulated RL71 in a xenograft model of triple negative breast cancer. J Biol Methods [Internet]. 2019Dec.16 [cited 2021Oct.21];6(4):e121. Available from: https://jbmethods.org/jbm/article/view/306

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