# Algorithms for Drug Sensitivity Prediction
## 6 Challenges  
This section discusses several challenges that require attention before personalized medicine becomes the standard of care in clinical oncology. Inconsistencies in data generation: Cell line studies have played a prominent role in analyzing personalized medicine approaches. However, some cell line-based studies have shown inconsistent results. For instance, a pair of recently-published databases, the Cancer Cell Line Encyclopedia (CCLE) [23] and the Cancer Genome Project (CGP) [24], provide a collection of genomic characterization and drug response results for \(>\)900 cell lines and 24 drugs and \(>\)700 cell lines and 138 drugs, respectively. A study [136] published in the following year observed concordance in genomic characterizations, but discrepancies in drug response data for the two databases. They analyzed the gene expression profiles of 471 cell lines common to both studies and observed high correlation with a median correlation coefficient of 0.86 for identical cell lines from the two databases as compared to different cell lines from the same database in spite of the use of different array platforms for gene expression measurements. Similar high correlation was observed in the presence of the mutation dataset in 64 genes in the 471 common cell lines. However, the correlations between responses in the two databases for common cell lines were observed to be limited for the majority of the drugs. [PERSON]'s rank correlation coefficients were calculated for both Area Under the Curve (AUC) and \(IC_{50}\) cell response measurements and observed to be \(<\)0.6 for the set of 15 common drugs [136]. Studies on these two major pharmacogenomics databases show that we need to be careful while using the available drug sensitivity databases, as alterations in the cell lines or differences in the assay and protocol used in different laboratories can produce discrepancies in the data. The majority of high throughput genomic measurements have been standardized based on the work in the last two decades, resulting in higher concordance in genomic measurements from different studies, whereas high throughput drug screen studies are yet to be appropriately normalized, resulting in the observed variations. One potential solution is to design transformation mappings that can map the results of one database into another one where the correlations between the studies are higher. Furthermore, it is expected that standardized approaches and environments for cell viability measurements along with a reduction in measurement noises will decrement the kind of inconsistencies observed in some of the current databases. Precision and accuracy: The prediction accuracy of the current state of the art techniques for drug sensitivity prediction is still limited [23; 25; 48] and often based on a single snapshot of the underlying system. The prediction accuracy can potentially be increased by incorporating diverse genomic  
Figure 6: **(a)** Protein and drug interactions among top features selected in the RPPAdata; **(b)** Outer ring showing details of the drug connection with CDK2; **(c)** Inner ring detail showing the CDK2 drug connectivity network from both the inner and outer rings. Images obtained from Cytoscape using the CyTargetLinker App (version 3.0.1). Only the elements marked with an \({}^{\star}\) are displayed in this network. characterization datasets, including DNA mutations, gene expression and protein expression along with epigenomic characterizations. Furthermore, the tumor microenvironment as observed through metabolomics can provide valuable complementary information. Irrespective of these improvements, additional information on the time domain behavior of the tumor response or the actual response to multiple perturbations of the individual tumor can be highly crucial in improving prediction accuracy.