01.12.2016 - 12:00

Improved consistency in drug response profiling with assay standardisation

FIMM researchers have demonstrated that it is possible to achieve good consistency between laboratories for drug response measurements by paying careful attention to the harmonisation of laboratory assays, and by using similar experimental and computational procedures.

In late 2013, a striking report was published in Nature, in which the authors cross-compared published data sets from the Broad and Sanger institutes in overlapping cancer cell lines. Their conclusion was that while gene expression profiles were consistent, drug sensitivity measurements showed highly discordant results. This study highlighted reproducibility issues in cancer pharmacogenomics, thereby echoing the recent concerns on the general lack of reproducibility of pre-clinical results, but at the same questioned the feasibility of functional profiling-based precision oncology.

Compelled to find out if this really is the case for all drug sensitivity measurements, a group of FIMM researchers started to compare the Sanger and Broad datasets against FIMM drug response data.  They confirmed the poor consistency between the Broad and Sanger drug responses, but also observed a good level of consistency between the FIMM and Broad drug response results. The improved consistency originated from similarities in the assays, both experimental and computational procedures, and importantly, from using the same comparison setup.

These new results, published in Nature as Brief Communications Arising article today December 1st, show that it is possible to achieve good inter-laboratory concordance in drug response measurements.

“Even though we have seen very high reproducibility at FIMM in our high-throughput drug response profiling studies, it was important to show that also between-laboratory reproducibility is indeed achievable”, commented Professor Tero Aittokallio from FIMM, the lead author of the study.

The results also indicate that the use of comparable laboratory and computational methods is critical for improved consistency.

“It will be important to develop global standards, similar to the MIAME standard for microarray data, not only for the laboratory assays but also for the computational procedures being used in drug data processing”, continued John Patrick Mpindi, a bioinformatician who was responsible for the data analyses.

The comparative work by FIMM provides a more positive view on the reproducibility of drug response screening compared to some of the recent publications, and hence supports the potential of pharmacogenomics and precision medicine, provided that sufficient standardisation of drug sensitivity assays is achieved.

Original publication:

John Patrick Mpindi, Bhagwan Yadav, Päivi Östling, Prson Gautam, Disha Malani, Astrid Murumägi, Akira Hirasawa, Sara Kangaspeska, Krister Wennerberg, Olli Kallioniemi and Tero Aittokallio. Consistency in drug response profiling. Nature, Brief Communications Arising, doi:10.1038/nature20171

More information:

Professor Tero Aittokallio

Institute for Molecular Medicine Finland (FIMM)

University of Helsinki, Finland

Tel: +358 50 318 2426

Email: tero.aittokallio@fimm.fi


Further reading:

Tero Aittokallio’s recent blog post

Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJ, Quackenbush J. Inconsistency in large pharmacogenomic studies. Nature. 2013 Dec 19;504(7480):389-93. doi: 10.1038/nature12831.

Cancer Cell Line Encyclopedia Consortium; Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature. 2015 Dec 3;528(7580):84-7. doi: 10.1038/nature15736.

Haverty PM, Lin E, Tan J, Yu Y, Lam B, Lianoglou S, Neve RM, Martin S, Settleman J, Yauch RL, Bourgon R. Reproducible pharmacogenomic profiling of cancer cell line panels. Nature. 2016 May 18;533(7603):333-7. doi: 10.1038/nature17987.                                                                                               

Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, Majumder MM, Malani D, Murumägi A, Knowles J, Porkka K, Heckman C, Kallioniemi O, Wennerberg K, Aittokallio T. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci Rep. 2014 Jun 5;4:5193. doi: 10.1038/srep05193. 

Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature 2014, 505: 612-613. doi: 10.1038/505612a

Last updated: 01.12.2016 - 12:54