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Cancer pharmacogenomics is the study of how variances in the genome influences an individual’s response to different cancer drug treatments. It is a subset of the broader field of pharmacogenomics, which is the area of study aimed at understanding how genetic variants influence drug efficacy and toxicity [1].

Cancer is a genetic disease where changes to genes can cause cells to grow and divide out of control. Each cancer can have a unique combination of genetic mutations, and even cells within the same tumour may have different genetic changes. In clinical settings, it has commonly been observed that the same types and doses of treatment can result in substantial differences in efficacy and toxicity across patients. [2, 3]. Thus, the application of pharmacogenomics within the field of cancer can offer key advantages for personalizing cancer therapy, minimizing treatment toxicity, and maximizing treatment efficacy. This can include choosing drugs that target specific mutations within cancer cells, identifying patients at risk for severe toxicity to a drug, and identifying treatments that a patient is most likely to benefit from [4, 5].

Applying pharmacogenomics within cancer has considerable differences compared to other complex diseases, as there are two genomes that need to be considered - the germline and the tumour. The germline genome considers inter-individual inherited genetic variations, and the tumour genome considers any somatic mutations that accrue as a cancer evolves [5, 6]. The accumulation of somatic mutations within the tumour genome represents variation in disease, and plays a major role in understanding how individuals will respond to treatments. Additionally, studies have shown that treatment response may also result from heritable traits, and thus differences within the germline genome should also be considered [7, 8].

Strategies
Advances in cancer diagnostics and treatment have shifted the use of traditional methods of physical examination, in vivo, and histopathological analysis to assessment of cancer drivers, mutations, and targetable genomic biomarkers [23]. There are an increasing number of genomic variants being studied and identified as potential therapeutically actionable targets and drug metabolism modifiers [24, 25]. Thus, a patient's genomic information, in addition to information about the patient's tumour, can be used to determine a personalized approach to cancer treatment [23, 26].

Cancer-Driving DNA Alterations
Cancer-driving DNA alterations can include somatic DNA mutations and inherited DNA variants. They are not a direct focus of pharmacogenomic studies, but they can have an impact on pharmacogenomic strategies [23]. These alterations can affect the pharmacokinetics and pharmacodynamics of metabolic pathways, making them potentially actionable drug-targets.

As whole-genome technologies continue to advance, there will be increased opportunities to discover mutations and variants that are involved in tumour progression, response to therapy, and drug-metabolism.

Candidate Polymorphism Search
Candidate polymorphism search refers to finding polymorphic DNA sequences within specific genes that are candidates for certain traits. Within pharmacogenomics, this method tries to resolve pharmacokinetic or pharmacodynamic traits of a compound to a candidate polymorphism level [23, 27]. This type of information can contribute to selecting effective therapeutic strategies for a patient.

To understand the potential functional impact of a polymorphic DNA sequence, gene silencing (link wikipedia page) can be used. Previously, siRNAs have been commonly used to suppress gene expressions, but more recently, shRNA have been suggested for use in studying and developing therapeutics [28, 29].

Another new method being applied is Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) (link wikipedia article). CRISPR, combined with the Cas9 enzyme, form the basis for the technology known as CRISPR-Cas9. This system can recognize and cleave specific DNA sequences, and thus is a powerful system for gene silencing purposes [30].

Candidate Pathway Search
An extension on the previous strategies is candidate pathway search. This type of analysis considers a group of related genes, whose altered function may have an effect on therapy, rather than solely focusing on one gene. It can provide insight into additional information such as gene-gene interactions, epistatic effects, or influences from cis-regulatory elements [23, 31]. These all contribute to understanding variations in drug efficacy and toxicity between patients.

HER2
HER2 is an established therapeutic target within breast cancer, and the activation of HER2 is observed in approximately 20% of breast cancers as a result of overexpression [13, 16]. Trastuzumab, the first HER2-targeted drug developed in 1990, interferes with HER2 signalling. In 2001, a study showed that adding trastuzumab to chemotherapy improved overall survival in women with HER2-positive metastatic breast cancer [14]. Then, in 2005, it was shown that trastuzumab is effective as an adjuvant treatment in women with early-stage breast cancer [13, 15]. Thus, trastuzumab has been a standard-of-care treatment in both metastatic and early stage HER2-postive breast cancer cases. Many genome sequencing studies have also revealed  that other cancer tumours had HER2 alterations, including overexpression, amplifications and other mutations [17, 18, 19, 20]. Because of this, there has been a lot of interest in studying the efficacy of HER2-targeted therapies within a range of cancer types, including bladder, colorectal, and gastro-esophageal.

BRC-ABL
The majority of chronic myelogenous leukemia cases are caused by a rearrangement between chromosomes 9 and 22. This results in the fusion of the genes BCR and ABL. This atypical gene fusion encodes for unregulated tyrosine kinase activity, which results in the rapid and continuous division of white blood cells [16, 21]. Drugs known as tyrosine kinase inhibitors target BCR-ABL, and are the standard treatment for chronic myelogenous leukemia. Imatinib was the first tyrosine kinase inhibitor discovered with high specificity for targeting BCR-ABL [22]. However, after imatinib was used as the first-line therapy, several BCR-ABL-dependent and BCR-ABL-independent mechanisms of resistance developed. Thus, new second-line and third-line drugs have also been developed to address new, mutated forms of BCR-ABL. These include dasatinib, nilotinib, bosutinib, and ponatinib [21].

Challenges
One of the biggest challenges in using pharmacogenomics to study cancer is the difficulty in conducting studies in humans. Drugs used for chemotherapy are too toxic to give to healthy individuals, which makes it difficult to perform genetic studies between related individuals [6]. Furthermore, some mutations occur at high frequencies, whereas others occur at very low frequencies, so there is often a need to screen a large number of patients in order to identify those with a particular genetic marker. And, although genomic-driven analyses is effective for stratifying patients and identifying possible treatment options, it is often difficult for laboratories to get reimbursed for these genomic sequencing tests. Thus, tracking clinical outcomes for patients whom undergo sequencing is key to demonstrating both the clinical utility and cost-effectiveness of pharmacogenomics within cancer [9].

Another challenge is that cancer patients are often treated with different combinations and dosages of drugs, so finding a large sample of patients that have been treated the same way is rare. So, studying the pharmacogenomics of a specific drug of interest is difficult, and, because additional identical trials may not be feasible, it can be difficult to replicate discoveries [1].

Furthermore, studies have shown that drug efficacy and toxicity are likely multigenic traits. Since pathways contain multiple genes, various combinations of driver mutations could promote tumour progression [9, 10, 11]. This can make it difficult to distinguish between functional driver mutations versus random, nonfunctional mutations [12].