Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

Pozdeyev, Nikita; Yoo, Minjae; Mackie, Ryan S.; Schweppe, Rebecca E.; Tan, Aik Choon; Haugen, Bryan R.

doi:10.18632/oncotarget.10010

Cited by 52 publications

(46 citation statements)

References 13 publications

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“…They further showed that increased fetal bovine serum and seeding cell density had a systematic effect on mean cell viability. Pozdeyev et al showed that restricting the computation of AUC to the concentration range shared between GDSC and CCLE, the equivalent of our AUC* drug sensitivity measure, yielded a small, but statistically significant improvement in consistent of pharmacological profiles 28 . Ultimately what our analysis and these recent reports suggest is that not only drug sensitivity measurements must be carefully and appropriately compared, but also that there is a pressing need for more robust pharmacological assays and standardized computational methods for modeling drug response.…”

Section: Discussionmentioning

confidence: 94%

Revisiting inconsistency in large pharmacogenomic studies

et al. 2017

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In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

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Section: Discussionmentioning

confidence: 94%

Revisiting inconsistency in large pharmacogenomic studies

et al. 2017

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“…In summary, as others have reported (30), discrepancies in the AUC measurements between the two studies is the major factor contributing to discordant observations. Despite this, we conclude that the majority of significant correlations that we report here are valid.…”

Section: Concordance Between the Two Datasetsmentioning

confidence: 60%

Expression Levels of Therapeutic Targets as Indicators of Sensitivity to Targeted Therapeutics

Roy

Winteringham

Lassmann

et al. 2019

Molecular Cancer Therapeutics

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Cancer precision medicine aims to predict the drug likely to yield the best response for a patient. Genomic sequencing of tumors is currently being used to better inform treatment options; however, this approach has had a limited clinical impact due to the paucity of actionable mutations. An alternative to mutation status is the use of gene expression signatures to predict response. Using data from two largescale studies, The Genomics of Drug Sensitivity of Cancer (GDSC) and The Cancer Therapeutics Response Portal (CTRP), we investigated the relationship between the sensitivity of hundreds of cell lines to hundreds of drugs, and the relative expression levels of the targets these drugs are directed against. For approximately one third of the drugs considered (73/222 in GDSC and 131/360 in CTRP), sensitivity was significantly correlated with the expression of at least one of the known targets. Surprisingly, for 8% of the annotated targets, there was a significant anticorrelation between target expression and sensitivity. For several cases, this corresponded to drugs targeting multiple genes in the same family, with the expression of one target significantly correlated with sensitivity and another significantly anticorrelated suggesting a possible role in resistance. Furthermore, we identified nontarget genes that are significantly correlated or anticorrelated with drug sensitivity, and find literature linking several to sensitization and resistance. Our analyses provide novel and important insights into both potential mechanisms of resistance and relative efficacy of drugs against the same target.

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“…the area over the graph of relative growth inhibition vs. drug concentration. Previous studies based on CCLE data found that this metric was one of the most informative 31,[40][41][42] , so we adopted it for our analysis. Two sources of information were available to quantify the genetic load: copy number changes and point mutations.…”

Section: Resultsmentioning

confidence: 99%

“…CCLE generated eight-point dose-response curves for each of the 24 compounds using an automated compound-screening platform 31 . We used the drug activity area as a measure of drug response because previous studies found it most informative 31,[40][41][42] .…”

Section: Ccle Datamentioning

confidence: 99%

Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia

Pavel

Korolev

2017

Sci Rep

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Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, which provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong, with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.

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Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

Cited by 52 publications

References 13 publications

Revisiting inconsistency in large pharmacogenomic studies

Revisiting inconsistency in large pharmacogenomic studies

Expression Levels of Therapeutic Targets as Indicators of Sensitivity to Targeted Therapeutics

Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia

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