The results of this study have several important implications for the field of cancer pharmacogenomics. Heritability estimates varied widely across the drugs studied, which implies quantitative differences in the role that genetic polymorphism plays in response to chemotherapeutic agents. The majority of drugs (66%) had a maximum heritability of >0.3, a threshold value for many genetic studies of complex human traits. This provides an objective basis for the prioritization of whole genome discovery studies in valuable clinical trial DNA samples. Indeed, if the results of the study presented here using this model system can be extended to future in vivo studies, the application of genome wide association studies to a drug with low heritability is unlikely to produce significant results.
By using multiple drugs within and across classes, evaluating common and unique pQTLs across drug classes may reveal important aspects of the genetic etiology of drug response. In addition, as replication has become the gold standard in human genetics applications, evaluation of different dose points for a single drug, other members of drug classes or structurally similar drugs could provide a measure of internal replication for the pQTLs identified. This replication approach can be used to help prioritize regions for follow-up in future studies. Similarly, further study of pQTLs that are common within and across drug classes may help elucidate potential unifying downstream mechanisms of action of the drugs. However, given our limited power to determine genome-wide significant linkage peaks, it should be noted that the results of the current study should be evaluated in future, well powered studies for finer genetic mapping.
As with any model system, the limitations of the system must be considered when interpreting the results. This in vitro model of drug-induced cytotoxicity simplifies the effect of these drugs on cancer patients in clinical settings, where pharmacokinetic and other factors will also play a role in patient response. For the results of this study to have clinical relevance, the genetic pathways of chemotherapeutic response in host or tumor response in vivo would need to be similar to what was seen in this study. Unfortunately, performing a study with similar aims to the one presented here using intact, extended human families would not be feasible due to ethical, logistical and cost hurdles.
While LCLs have been frequently used as a model system for uncovering the genetics of gene expression [12
], radiation response [13
] and drug induced cytotoxicity [4
], a recent study [8
] highlighted the need for proper study design and interpretation of the data. In order to limit the effect of technical and biological noise on the results of this study, each drug and dose measurement was performed in quadruplicate and was then repeated from a separately seeded culture of LCLs. Technical and biological reproducibility for the assay was extremely high, with a strong correlation among replicate measures (r = 0.9067). In addition, the average coefficient of variation for all drugs and vehicle controls across all replicates was 6.57% (standard deviation = 6.28%), indicating low variation across replicates. We also included baseline growth rate, calculated for each vehicle control as a factor in our variance components analysis, which was itself minimally heritable (h2
< 0.14). Furthermore, testing for over-representation of pQTLs within drug classes suggests that drugs with similar chemical structures had similar cytotoxic response, indicating that LCL cytotoxicity was specific to each drug and not an artifact of the experimental design. Unfortunately, we did not test for Ebstein–Barr virus copy number levels in our assay, and thus this remains a limitation of the study.
The results of this study lay the groundwork for future studies to uncover the underlying genes and variants influencing chemotherapeutic response. Whole-genome association studies, using LCLs or DNA derived from clinical trials should be undertaken for drugs found to have the highest heritability. This phenotype selection approach provides a clear justification for where investment in extensive genomic analysis should be prioritized to reach the goal of effective selection of therapy for individual patients.