# Algorithms for Drug Sensitivity Prediction
## 2 Data Characterizations  
A crucial preliminary step in drug sensitivity prediction analysis involves collecting information on various aspects of the biological system. The observations can involve internal genetic states of the systems, such as mutation and gene expression, or phenotypic changes following perturbations to the system, such as cell viability measurement after applying various drugs. This section will provide an overview of genomic and functional characterizations that are commonly used in drug sensitivity prediction studies. A pictorial representation of the various data types is shown in Figure 1.  
Genomic characterization data can be derived from different levels of the cell, such as the genome, transcriptome, proteome or metabolome, with each level playing a specific role in the conversion of information coded in DNA to functional activities in the cell. The levels can potentially provide diverse sets of information, such as mutations in the genome or altered transcriptional behavior, that can assist in predicting the sensitivity of the tumor to a drug. For instance, a commonly-considered genetic test for breast cancer patients involves the measurement of HER2 protein expression. HER2 overexpressed patients are usually prescribed therapy with either of the targeted drugs trastuzumab, pertuzumab or lapatinib that interfere with the HER2 receptor [5; 6]. However, it has been observed that not all patients with HER2 overexpression measured by immunohistochemistry show gene amplification as when measured by fluorescence in situ hybridization [7; 8; 9]. Thus, it is highly relevant to explore the various levels of biological activities in a cell, as sensitivity to a drug may be explained by different combinations of activities at various genomic levels. At the DNA level, Single Nucleotide Polymorphisms (SNPs) denote variations in a single nucleotide block. As an example, an SNP may denote replacement of cytosine (C) by thymine (T) in a stretch of DNA. Copy Number Variations (CNVs) signify duplications of stretches of DNA and can denote several copies of a gene. SNPs and CNVs are biomarkers that play an important role in analyzing the response to drugs, as an over-amplification of some genes as captured by CNV measurements can represent the activation of oncogenes. As an example, a higher copy number of the EGFR gene has been observed for some non-small-cell lung cancer patients, and the information can be used to predict sensitivity to the targeted EGFR inhibitor drug gefitinib [10]. Similarly, deletions or mutations in tumor suppressor genes, as captured by CNV or SNP measurements, may lead to loss of function resulting in tumor growth. At the epigenetic level, DNA methylation is the process of methyl groups attaching to DNA that can alter the transcription process. A form of aberrant DNA methylation observed in cancer involves inactivation of tumor suppressor genes by hyper-methylation of the large CpG dinucleotide clusters located in their promoter islands [11]. These aberrations are often targeted by DNA-demethylating agents [12; 13]. At the transcriptomic level, RNA expression is measured by various profiling techniques, such as microarray [14], SAGE [15], MPSS [16] and RNA-Seq [17], and each technology has its own benefits and concerns. A common example of gene expression application to guide cancer therapy includes the usage of trastuzumab for the overexpression of HER2 (ERBB2) in breast cancer patients. It is quite often the case that observations from the proteomic level using technologies, such as the 2D gel electrophoresis technique (2D-GE) [18], MALDI imaging mass spectrometry [19] or reverse-phase protein array [20], are also considered for anti-cancer therapeutic decision making. Another level of information observed through metabolites [21] is currently being considered to provide unique insights on the potential sensitivity of drugs, as the metabolism of cancerous cells is quite different from normal cells.