# Algorithms for Drug Sensitivity Prediction
## 2 Data Characterizations
Figure 1: Pictorial representation of the commonly-used genomic and functional characterizations and data types used for drug sensitivity prediction. A number of separate large-scale experimental studies have been conducted on cancer cell lines or patient tumor cultures to observe the effect of applied drugs and results subsequently published as pharmacogenomics databases. NCI60 [22] has applied CDNA microarrays to observe the variation among 8000 genes in 60 cell lines for anti-cancer drugs and provides DNA copy number variation, mutation and protein expression information. In the Cancer Cell Line Encyclopedia (CCLE) [23] database, gene expression and Single Nucleotide Polymorphism (SNP6) expressions are included for about 18, 988 and 23, 316 genes respectively, for 947 human cancer cell lines, along with sensitivities for 24 drugs. While in the Cancer Genome Project (CGP) [24], 75,000 experiments have been conducted to generate the response of 138 anti-cancer drugs across >700 cancer cell lines and gene expression, mutation and CNV information for >23,000 genes. Additionally, a collaborative NCI and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) initiative [25] to predict drug response based on a group of genomic, epi-genomic and proteomic profiling datasets provided six characterizations (gene expression, methylation, RNA sequencing, exome sequencing, Reverse Phase Protein Array (RPPA) and SNP6) for 53 breast cancer cell lines and 35 anti-cancer drugs. The Cancer Genome Atlas (TCGA) [26] has profiled and analyzed large numbers of human patient tumors to find the molecular aberrations among genes with proteomic and epigenetic expression. The dataset includes a total of 5074 tumor samples, out of which 93% has been assessed for RPPA, DNA methylation, copy number, mutation, microRNA and gene expression, but they are yet to include drug sensitivity for anti-cancer drugs. Responses of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents have been reported in the GRAY database [27]. The dataset includes characterizations of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. The Genentech (GNE) database [28] describes RNA sequencing and SNP array analysis of 675 human cancer cell lines along with responses to a PI3K inhibitor (GDC-0941) and a MEK inhibitor (GDC-0973). While in the Personal Genome Project (PGP) database [29], the PF-03814735 inhibitor of Aurora kinases was tested in vitro in a panel of 87 cancer lines derived from human lung, breast and colorectal tumors, where characterizations of mutation and gene expression data were determined. The Cancer Therapeutics Response Portal (CTRP) [30] contains the response of 860 deeply-characterized cancer cell lines to 481 small-molecule probes that target specific nodes of important cellular processes. The preceding description considered various genomic characterizations of a tumor cell line or culture, and we next discuss various approaches to measure the response of tumors to various drugs. The drug responses are usually observed using pharmacological assays that measure metabolic activity in terms of reductase-enzyme product or energy-transfer molecule ATP levels following 72-84 h of drug delivery. The cell viability drug screenings are usually conducted using a robotic system where drugs are delivered to small wells containing a portion of the cell culture of interest. Examples of such commercial sellers include GNF Systems ([[http://gnfsystems.com](http://gnfsystems.com)]([http://gnfsystems.com](http://gnfsystems.com))) and Wako Automation ([[http://www.wakoautomation.com](http://www.wakoautomation.com)]([http://www.wakoautomation.com](http://www.wakoautomation.com))). Each well is expected to contain more than 100 cells. Experiments are conducted with multiple drug concentrations (around 5-10 concentrations), and the luminescence in each well is measured at the steady state to assess cell viability.