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Cited by 48Statistical Methods in Clinical TrialsAnimal testing and alternativesOptimal Experimental Design Methods

Optimized design and analysis of preclinical intervention studies in vivo

Open access
Laajala, Teemu D., Jumppanen, Mikael, Huhtaniemi, Riikka, Fey, Vidal, Kaur, Amanpreet, Knuuttila, Matias, Aho, Eija, Oksala, Riikka +4 more·2016·Scientific Reports
DOI 10.1038/srep30723PMC PMC4969752
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Abstract

Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.

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  • Mice were allocated according to body weights to three study groups using a published algorithm.

    “Mice were allocated according to body weights to three study groups MDA-MB-231 CIP2A_Control (n = 7), MDA-MB-231 CIP2A_K21A Clone1 (n = 8) and MDA-MB-231 CIP2A_K21A_Clone2 (n = 8), using published algorithm64.”

    From Structural mechanism for inhibition of PP2A-B56α and oncogenicity by CIP2A (2023) · cites this paper as [64]

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References 48 · Cited by 48

Research works printed but not linked: 35

  1. Collins F. S. & Tabak L. A. Policy: NIH plans to enhance reproducibility. Nature 505, 612– 613, doi: 10.1038/505612a (2014). 24482835 PMC4058759 · DOI · Paper page
  2. Henderson V. C.. A meta-analysis of threats to valid clinical inference in preclinical research of sunitinib. Elife. 4, 1– 13, doi: 10.7554/eLife.08351 (2015). PMC4600817 26460544 · DOI
  3. Singh M. & Ferrara N. Modeling and predicting clinical efficacy for drugs targeting the tumor milieu. Nat Biotechnol 30, 648– 657, doi: 10.1038/nbt.2286 (2012). 22781694 · DOI
  4. Couzin-Frankel J. When mice mislead. Science 342, 922– 925, doi: 10.1126/science.342.6161.922 (2013). 24264972 · DOI
  5. Perrin S. Preclinical research: make mouse studies work. Nature 507, 423– 425, doi: 10.1038/507423a (2014). 24678540 · DOI · Paper page
  6. Henderson V. C., Kimmelman J., Fergusson D., Grimshaw J. M. & Hackam D. G. Threats to validity in the design and conduct of preclinical efficacy studies: a systematic review of guidelines for in vivo animal experiments. PLoS Med 10, e1001489, doi: 10.1371/journal.pmed.1001489 (2013). 23935460 PMC3720257 · DOI
  7. Cressey D. UK funders demand strong statistics for animal studies. Nature 520, 271– 272, doi: 10.1038/520271a (2015). 25877180 · DOI
  8. Macleod M. Why animal research needs to improve. Nature 477, 511, doi: 10.1038/477511a (2011). 21956292 · DOI
  9. Hildebrand F.. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol 14, R4, doi: 10.1186/gb-2013-14-1-r4 (2013). 23347395 PMC4053703 · DOI
  10. Hasty A. H. & Gutierrez D. A. What have we really learned about macrophage recruitment to adipose tissue? Endocrinology 155, 12– 14, doi: 10.1210/en.2013-2027 (2014). 24364586 PMC3868801 · DOI
  11. Reardon S. A mouse’s house may ruin experiments. Nature 530, 264, doi: 10.1038/nature.2016.19335 (2016). 26887470 · DOI
  12. Kilkenny C., Browne W. J., Cuthill I. C., Emerson M. & Altman D. G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol 8, e1000412, doi: 10.1371/journal.pbio.1000412 (2010). 20613859 PMC2893951 · DOI · Paper page
  13. Baker D., Lidster K., Sottomayor A. & Amor S. Two years later: journals are not yet enforcing the ARRIVE guidelines on reporting standards for pre-clinical animal studies. PLoS Biol 12, e1001756, doi: 10.1371/journal.pbio.1001756 (2014). 24409096 PMC3883646 · DOI · Paper page
  14. Sugar E., Pascoe A. J. & Azad N. Reporting of preclinical tumor-graft cancer therapeutic studies. Cancer Biol Ther 13, 1262– 1268, doi: 10.4161/cbt.21782 (2012). 22895077 PMC3493433 · DOI
  15. Eisen J. A., Ganley E. & MacCallum C. J. Open science and reporting animal studies: who’s accountable? PLoS Biol 12, e1001757, doi: 10.1371/journal.pbio.1001757 (2014). 24409097 PMC3883631 · DOI
  16. Day C. P., Merlino G. & Van Dyke T. Preclinical mouse cancer models: a maze of opportunities and challenges. Cell 163, 39– 53, doi: 10.1016/j.cell.2015.08.068 (2015). 26406370 PMC4583714 · DOI · Paper page
  17. Su Z. Optimal allocation of prognostic factors in randomized preclinical animal studies. Drug Inf J 45, 725– 729, doi: 10.1177/009286151104500508 (2011). · DOI
  18. Kasturi J.. IRINI: random group allocation of multiple prognostic factors. Contemp Clin Trials 32, 372– 381, doi: 10.1016/j.cct.2010.12.009 (2011). 21187163 · DOI
  19. Knuuttila M.. Castration induces up-regulation of intratumoral androgen biosynthesis and androgen receptor expression in an orthotopic VCaP human prostate cancer xenograft model. Am J Pathol 184, 2163– 2173, doi: 10.1016/j.ajpath.2014.04.010 (2014). 24949550 · DOI
  20. Hutchinson L. & Kirk R. High drug attrition rates—where are we going wrong? Nat Re Clin Oncol 8, 189– 190, doi: 10.1038/nrclinonc.2011.34 (2011). 21448176 · DOI · Paper page
  21. Treasure T. & MacRae K. D. Minimisation: the platinum standard for trials? Randomisation doesn’t guarantee similarity of groups; minimisation does. BMJ 317, 362– 363 (1998). 9694748 PMC1113668
  22. Smith G. D. & Ebrahim S. Data dredging, bias, or confounding: They can all get you into the BMJ and the Friday papers. BMJ 325, 1437– 1438 (2002). 12493654 PMC1124898
  23. van der Worp H. B.. Can animal models of disease reliably inform human studies? PLoS Med 7, e1000245, doi: 10.1371/journal.pmed.1000245 (2010). 20361020 PMC2846855 · DOI · Paper page
  24. Seashore-Ludlow B.. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov 5, 1210– 1223, doi: 10.1158/2159-8290.CD-15-0235 (2015). 26482930 PMC4631646 · DOI · Paper page
  25. Wessel J. & Schork N. J. Generalized genomic distance-based regression methodology for multilocus association analysis. Am J Hum Genet 79, 792– 806, doi: 10.1086/508346 (2006). 17033957 PMC1698575 · DOI
  26. Pond G. R., Tang P. A., Welch S. A. & Chen E. X. Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials. Clin Trials 7, 227– 234, doi: 10.1177/1740774510368301 (2010). 20406744 · DOI
  27. Greevy R., Lu B., Silver J. H. & Rosenbaum P. Optimal multivariate matching before randomization. Biostatistics 5, 263– 275, doi: 10.1093/biostatistics/5.2.263 (2004). 15054030 · DOI
  28. Lu B., Greevy R., Xu X. & Beck C. Optimal nonbipartite matching and its statistical applications. Am Stat 65, 21– 30, doi: 10.1198/tast.2011.08294 (2011). 23175567 PMC3501247 · DOI
  29. Pond G. Statistical issues in the use of dynamic allocation methods for balancing baseline covariates. Br J Cancer 104, 1711– 1715, doi: 10.1038/bjc.2011.157 (2011). 21540857 PMC3111167 · DOI
  30. McCane B. & Albert M. Distance functions for categorical and mixed variables. Pattern Recognit Lett 29, 986– 993, doi: 10.1016/j.patrec.2008.01.021 (2008). · DOI
  31. Wilson D. R. & Martinez T. R. Improved heterogeneous distance functions. J Artif Intell Res 6, 1– 34, doi: 10.1613/jair.346 (1997). · DOI
  32. Zhao L.. Bayesian hierarchical changepoint methods in modeling the tumor growth profiles in xenograft experiments. Clin Cancer Res 17, 1057– 1064, doi: 10.1158/1078-0432.CCR-10-1935 (2011). 21131555 PMC3048927 · DOI
  33. Laajala T. D.. Improved statistical modeling of tumor growth and treatment effect in preclinical animal studies with highly heterogeneous responses in vivo. Clin Cancer Res 18, 4385– 4396, doi: 10.1158/1078-0432.CCR-11-3215 (2012). 22745104 · DOI
  34. Pinheiro J. C. & Bates D. M. Hypothesis tests and confidence intervals in Mixed effects models in S and S-PLUS (eds. Chambers J..) Ch. 2.4, 82– 96 (Springer-Verlag, 2000).
  35. Gelman A. & Hill J. Sample size and power calculations in Data analysis using regression and multilevel/hierarchical models (eds. Alvarez R. M..) Ch. 20, 437– 454 (Cambridge University Press, 2007).

Authors
Laajala, T. D., Jumppanen, M., Huhtaniemi, R., Fey, V., Kaur, A., Knuuttila, M., Aho, E., Oksala, R., Westermarck, J., Mäkelä, S., Poutanen, M., & Aittokallio, T.
Affiliations
  • University of Helsinki (FI)
  • University of Turku (FI)
  • Orion Corporation (Finland) (FI)
Corresponding author
  • Poutanen, Matti
  • Aittokallio, Tero
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2016-08-02
Volume
6
Issue
1
Pages
30723-30723
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep30723
PMC
PMC4969752
OpenAlex
W2479785963
Cited by
48
References
48
Citations by year
2026: 2 · 2025: 5 · 2024: 5 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 8 · 2019: 7 · 2018: 1 · 2017: 4
FWCI
4.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Työväentutkimus Vuosikirja (submittedVersion, other-oa)
Funders
  • Finnish Cancer Institute
  • Suomen Kulttuurirahasto
  • National Cancer Institute (16X064)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords
preclinical animal studies, intervention study design, randomization, power analysis, prostate cancer xenografts, castration-resistant prostate cancer, sample size reduction, reproducibility, translatability, web application
MeSH terms
Animals, Humans, Male, Neoplasm Transplantation, Prostatic Neoplasms, Random Allocation, Research Design, Software, Reproducibility of Results, Models, Statistical, Sequence Analysis, RNA, Sample Size, Mice

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