Drug response consistency in CCLE and CGP

Bouhaddou, Mehdi; DiStefano, Matthew; Riesel, Eric A.; Carrasco, Emilce; Holzapfel, Hadassa Y; Jones, DeAnalisa C.; Smith, Gregory R.; Stern, Ari; Somani, Sulaiman; Thompson, Taylor V.; Birtwistle, Marc R.

doi:10.1038/nature20580

Cited by 67 publications

(58 citation statements)

References 3 publications

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“…With some important caveats, the amount of ATP in a lysate from a cell culture dish or well is proportional to the number of viable cells (Tolliday, 2010). Several large-scale datasets describing the responses of hundreds of cell lines to libraries of anticancer drugs have recently been published (Barretina et al, 2012;Garnett et al, 2012;Haverty et al, 2016;Seashore-Ludlow et al, 2015), but their reproducibility and utility is being debated (Bouhaddou et al, 2016;CCLE Consortium and GDSC Consortium, 2015;Haibe-Kains et al, 2013;Safikhani et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A multi-center study on factors influencing the reproducibility ofin vitrodrug-response studies

Niepel

Hafner

Williams

et al. 2017

Preprint

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Evidence that some influential biomedical results cannot be recapitulated has increased calls for data that is findable, accessible, interoperable, and reproducible (FAIR). Here, we study factors influencing the reproducibility of a prototypical cell-based assay: responsiveness of cultured cell lines to anti-cancer drugs. Such assays are important for drug development, mechanism of action studies, and patient stratification. This study involved seven research centers comprising the NIH LINCS Program Consortium, which aims to systematically characterize the responses of human cells to perturbation by gene disruption, small molecule drugs, and components of the microenvironment. We found that factors influencing the measurement of drug response vary substantially with the compound being analyzed and thus, the underlying biology. For example, substitution of a surrogate assay such as CellTiter-Glo® for direct microscopy-based cell counting is acceptable in the case of neratinib or alpelisib, but not palbociclib or etoposide. Uncovering and controlling for such context sensitivity requires systematic measurement of assay robustness in the face of biological variation, which is distinct from assay precision and sensitivity. Conversely, validating assays only over a narrow range of conditions has the potential to introduce serious systematic error in a large dataset spanning many compounds and cell lines.

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Section: Introductionmentioning

confidence: 99%

A multi-center study on factors influencing the reproducibility ofin vitrodrug-response studies

Niepel

Hafner

Williams

et al. 2017

Preprint

Self Cite

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“…One involved a large collaborative project that I led for nearly a decade -the Cancer Cell Line Encyclopedia. Reported inconsistencies with a related project, called the Genomics of Drug Sensitivity in Cancer, prompted intense, clarifying discussions on precisely how measurements are taken and on what sorts of data give the best insights for developing therapies [4][5][6][7][8][9][10][11][12][13][14][15] .…”

Section: Robustnessmentioning

confidence: 99%

Five ways consortia can catalyse open science

Cutcher‐Gershenfeld

Baker

Berente

et al. 2017

Nature

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“…Bouhaddou et al calculated a common viability metric across a shared log10-dose range, and computed slope, AUC values and found the new matrix could lead to better consistency. 21 Hafner et al proposed another metric called GR50 to summarize drug sensitivity and demonstrated its superiority in assessing the effects of drugs in dividing cells. 22 Most proposed ideas focused on forming better summarization metric and/or standardizing experiments and data processing pipeline.…”

Section: Introductionmentioning

confidence: 99%

AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets

Newbury

et al. 2018

Preprint

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The inconsistency of open pharmacogenomics datasets produced by different studies limits the usage of pharmacogenomics in biomarker discovery. Investigation of multiple pharmacogenomics datasets confirmed that the pairwise sensitivity data correlation between drugs, or rows, across different studies (drug-wise) is relatively low, while the pairwise sensitivity data correlation between cell-lines, or columns, across different studies (cell-wise) is considerably strong. This common interesting observation across multiple pharmacogenomics datasets suggests the existence of subtle consistency among the different studies (i.e., strong cell-wise correlation). However, significant noises are also shown (i.e., weak drug-wise correlation) and have prevented researchers from comfortably using the data directly. Motivated by this observation, we propose a novel framework for addressing the inconsistency between large-scale pharmacogenomics data sets. Our method can significantly boost the drug-wise correlation and can be easily applied to re-summarized and normalized datasets proposed by others. We also investigate our algorithm based on many different criteria to demonstrate that the corrected datasets are not only consistent, but also biologically meaningful. Eventually, we propose to extend our main algorithm into a framework, so that in the future when more data-sets become publicly available, our framework can hopefully offer a "ground-truth" guidance for references.

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Drug response consistency in CCLE and CGP

Cited by 67 publications

References 3 publications

A multi-center study on factors influencing the reproducibility ofin vitrodrug-response studies

A multi-center study on factors influencing the reproducibility ofin vitrodrug-response studies

Five ways consortia can catalyse open science

AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets

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