Introduction to Genomics and Pharmacogenomics:
The origin of genomics dates back to the 1900s. Hans Winkler coined the term genome, meaning the genetic make-up of the organism. Watson and Crick with their work on the structure of DNA marked another landmark in the field of molecular biology (1). The dideoxy sequencing method developed by Fred Sanger was used to sequence the genome of viruses. A set of genes was studied in depth with the advent of whole-genome sequencing. This was followed by the study of bacterial genomes and yeast genomes. The idea of sequencing the human genome was incentivized by the advancements in genome sequencing, mapping, and bioinformatics (2). Human Genome Project in 2001 accelerated the development of the human genome. Genomics has paved way for advanced diagnostics and therapies in the recent past (3). The knowledge of the human genome can predict the drug response of an individual, thus providing opportunities to develop personalized therapies.
Pharmacogenomics, a branch of pharmacology studies the impact of genetic variations on the efficacy of drugs. Besides lifestyle and environmental influences, the genetic make-up of the person is predicted to be decisive of the drug response (3). Understanding the genetic influences helps to determine the most effective and safe dose for every individual. The association of genotype and drug-induced phenotype can be used for patients’ benefit (4).
Role Of Pharmacogenomics In Cancer
Considering the growing cancer burden, the development of new therapies is essential. Chemotherapy-associated adverse events are a cause of concern. Chemotherapy toxicity can be predicted by genetic polymorphisms. An individual’s genetic profile affects the drug’s toxicity and response. The genotype is becoming pragmatic in the selection of effective therapies (5). Pharmacogenomics is pivotal to address the narrow therapeutic index of the drugs, drug resistance, high rate of recurrence, mortality, and the associated adverse events of chemotherapy. Pharmacogenomics-derived personalized medicine can lead to enhanced treatment outcomes in cancer patients (4). As more and more targeted anti-cancer drugs including monoclonal antibodies become available, the need to assess the impact of pharmacogenomics on the efficacy and safety of these new therapies is rising (6).
Somatic and Germline Mutations
Somatic mutations may be passengers or may play a role in defining the cancer subtypes. They activate proteins that have become a target for developing multiple targeted therapies (4). Germline mutations inherited from earlier generations affect the pharmacodynamics of the cancer drugs and are indicative of an individual’s drug response. In the recent past, several germline-based oncology drugs with sufficient evidence have received FDA approval.
Irinotecan and UGT1A1
Irinotecan toxicity depends on the UDP glucuronosyltransferase 1A1 (UGT1A1) genotype. Patients with UGT1A1*28 and UGT1A1∗6 have a higher risk of neutropenia and are advised lower dose of the drug (7).
Dihydropyrimidine dehydrogenase genotype and fluoropyrimidine
Although 5-Fluorouracil (5-FU), capecitabine, and tegafur are used widely in the treatment of head and neck cancers, breast cancer, and colorectal cancer, the growing drug resistance has limited their use. Deficiency of the dihydropyramidine dehydrogenase (DPD) enzyme, involved in the 5-FU metabolism causes myelosuppression, mucositis, neurotoxicity, and diarrhea. There are a few SNPs like E244V, A551T, DPYD*4, and DPYD*13 in the encoding gene DYPD that are associated with low DPD enzyme and high 5-FU toxicity (8). DPYD*2A, a predictive marker for the 5-FU toxicity is now routinely used.
Thymidylate synthetase (TS) And Methylenetetrahydrofolate reductase (MTHFR)
Two other enzymes critical in the 5-FU pathway are thymidylate synthethase (TS) and methylenetetrahydrofolate reductase (MTHFR). The promotor enhancer region (TSER) in the gene (TYSM) encoding TS is crucial for gene expression. TSER*3, an allele with a three-repeat sequence predicts poor survival in patients treated with 5-FU. 677C>T genetic variant in the MTHFR encoding gene is associated with the high toxicity of methothrexate (MTX) treatment (9). The TPMT gene regulates the metabolism of azathioprine, 6-mercaptopurine (6-MP), and 6-thioguanine drugs. These drugs are commonly used in acute lymphoblastic leukemia (ALL) treatment. The gene producing methylthionosine 5-prime monophosphate from 6-MP might lead to severe toxicity. The alleles, TPMT*2 and TPMT*3A are indicative of a high risk of thiopurine-associated toxicity (10).
G6PD and Rasburicase
Rasburicase is recommended for the treatment of hyperuricemia, a chemotherapy-induced event in patients with hematological malignancy. However, patients with glucose-6-phosphate dehydrogenase (G6PD) deficiency are not recommended the drug. Most G6PD genetic mutations are missense. Amongst the 5 categories of the variants (I-V), classes II and III are responsible for the majority of G6PD deficiencies. In G6PD deficient patients, rasburicase causes hemolytic anemia and, rarely, methemoglobinemia, for which, high oxygen flow, ascorbic acid, and blood transfusions are common treatments. Considering the impact of G6PD deficiency, G6PD sequencing is essential for diagnosis (11).
CYPD6 and Tamoxifen
Hormone-positive breast cancers are commonly treated with tamoxifen. Tamoxifen, a selective ER modulator, inhibits estrogen binding and slows the growth of ER-positive tumors. The hepatic enzyme cytochrome P450 2D6 (CYP2D6) drives the pharmacological activity of tamoxifen. Patients with low CYP2D6 activity reduce the benefit of tamoxifen (12). The common alleles reducing the efficacy of tamoxifen are CYP2D6*4, CYP2D6*3, CYP2D6*5, and CYP2D6*6.
Predictive Biomarkers and Targeted Therapies
Tyrosine kinase inhibitors are used in the treatment of multiple malignancies like colorectal cancer, breast cancer, non-small-cell lung cancer. Several prognostic biomarkers have been developed for the TKIs. Diagnostic tests for BRCA1/2, ALK, BRAF, KRAS, EGFR mutations are integrated into the clinical management. Additionally, next-generation sequencing for the predictive biomarker is routinely used. The predictive biomarkers have led to improved selection of cancer therapy (13). This has reduced the side effects and the medication duration (3).
Addressing the Barriers:
Pharmacogenomics is still a naïve field and its integration in clinical management has certain limitations. Consensus guidelines and infrastructure to train and educate clinicians are lacking. A clinical decision support tool to help clinicians’ application of pharmacogenomic information is not available. Additionally, the extended turnaround time of genetic results, pharmacogenomic-driven medications not being included in the insurance limits the use of pharmacogenomics in routine practice. The most important and common issue faced during chemotherapy remains to be drug resistance and associated adverse events (8). Many biomarkers are restricted to the tumor cells themselves. The biggest challenge lies in obtaining tumor tissue from patients with advanced disease who may not have biopsy-accessible tissue (6). The identification of biomarkers and invasive procedures like a biopsy is currently pivotal in the implementation of pharmacogenomics. Similarly, knowledge about intratumoral heterogeneity is essential for therapy selection (14). However, a liquid biopsy detecting the intrinsic intratumoral heterogeneity and mutations in neoplasia can help alter the drugs and guide the treatment (15). Furthermore, differentiating driver mutations and passenger mutations is crucial for selecting effective treatment. Global data integrating the genomic results and clinical outcomes is indispensable with the increasing use of pharmacogenomic-based medicines in therapeutics (16). Strengthening the health information technology, translational research and medicine, patient-reported outcomes, and large clinical trials will ensure matching of individual patient characteristics and genomics to provide the evidence (14).
Future Of Pharmacogenomics:
Despite the barriers, pharmacogenomics provides an excellent opportunity to offer effective treatments to cancer patients. Integrating multi-omics and the development of AI algorithms to mine data might be the future of predictive biomarker development (13). Additionally, the implementation of predictive biomarkers can assist the development of new drug trials(13). Moreover, more focus needs to be on implementing genetic testing to be able to utilize the pharmacological data (17). The knowledge about the drug response leading to personalized medicine can transform the treatment landscape. The evolution in the field using computerized systems to make precision medicine a reality will revolutionize cancer treatment.
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