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The Pharmacogenomics Landscape in Personalized Healthcare Space
From being one of the inaugural additions to the precision medicine initiative to getting eclipsed by advances in interrelated pan-omics, pharmacogenomics has evolved quickly.
The existence of DNA copy number variations even between monozygotic twins is proof that no two individuals are ever truly identical. Although diseases occur due to causative agents, the unique combination of genetic makeup, physiology, and external environment creates heterogeneity among populations in their response to disease onset, progression, and treatment.
This explains why the conventional “one-size-fits-all” approach of therapies is neither consistent nor foolproof. Pharmacogenetics (pharmacology + genetics), a term coined by Vogel in 1959 is the study of customizing drug choice and dosage based on the patient’s gene. But even before its inception, predictions on its arrival were made by Sir Archibald Garrod and JBS Haldane. While the former anticipated individual variations in his book “Inborn Factors in Diseases”, the latter foresaw that biochemical individuality could determine unusual reactions to drugs [1,2].
From Pharmacogenetics to Pharmacogenomics
Genetic variations in the metabolism of debrisoquine and sparteine were one of the initial studies which eventually led to the entry of pharmacogenetics into clinics. The lack of cytochrome liver enzyme CYP2D6 led to poor metabolism of not just the above-mentioned drugs but also 60 others . Further investigations revealed a number of CYP2D6 variants with different functionalities. This indicates how various single nucleotide polymorphisms (SNPs) of genes coding for drug-metabolizing enzymes, receptors or transporters could affect drug responses.
By the time the human genome project had accomplished whole-genome sequencing, the complexity rendered by gene network interactions and the variability in mRNA expression levels were well understood. This paved way for pharmacogenomics, where complete genomic expression profiles were inspected before drug administration. Moreover, advances in technologies such as microarray and genome-wide association studies (GWAS) added significant power to the budding field.
While complete details of patient history were taken into account before dosage prescriptions, pharmacogenomics also measures how drugs, in turn, influence gene expression patterns. A detailed list of pharmacogenomic biomarker information is now available for over 350 FDA-approved drugs providing key information on mechanisms of drug action and dosage choice, adverse reactions, etc. .
Warfarin Dosage Determination by Pharmacogenomics
Warfarin is an anticoagulant drug that is predominantly used for treating blood clotting in cases of stroke, pulmonary embolism, heart attacks or patients with artificial heart valves . However, the challenge that clinicians often encountered was deciding its dosage. Erroneous applications of warfarin cause adverse side effects, wherein high doses resulted in hemorrhage while low doses failed to cure thrombosis.
It is, therefore, crucial to maintaining accuracy on dosage after careful consideration of one’s age, race, weight, height, smoking status, etc. But the most significant of all is examining the SNPs of two major genes, Cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and Vitamin K epoxide reductase complex subunit 1 (VKORC1). These genes encode enzymes that aid in warfarin metabolism and in fact, the drug works by inhibiting the VKORC1 complex.
The polymorphisms of CYP2C9 and VKORC1 are estimated to cause approximately 18% and 25% variance in stable warfarin doses respectively [6,7]. Currently, an international normalized ratio (INR) of 2.0 to 3.0 is considered an effective therapeutic range for people taking warfarin .
Pharmacogenomic Testing in Precision Oncology
Besides cardiology, genetic testing has forayed into other therapeutic areas such as oncology, hematology, neurology, and psychiatry among others. Genetic testing of patients with hereditary breast and ovarian cancer (HBOC) syndromes traditionally scanned for mutations in the breast cancer susceptibility genes BRCA1 and BRCA2. Once, the US Supreme Court ruled that human genes are non-patentable, BRCA testing became more easily accessible . In recent times though, the relative ease and feasibility of genome sequencing have made laboratories turn to gene panel testing, where large numbers of genes are tested simultaneously for a much cheaper cost. Results of such tests reveal that in a few cases, disease-causing mutations were found on non-BRCA genes as well. This has led to the additional testing of other genes such as human epidermal growth factor receptor 2 (HER2), PTEN, TP53, CHEK-2, CYP19, and mismatch repair genes .
Oncotype Dx & MammaPrint – Genomic Tests that Transformed Clinical Practice
Patients diagnosed with lymph node- and HER2-negative, but hormone receptor-positive stage I or II invasive breast cancer are now recommended to undergo an Oncotype Dx tumor profiling test before directly opting for chemotherapy. Depending upon their low (0-10) or high (26-100) scores, chemotherapy is either avoided or included from the prescribed hormone therapy respectively.
The results of the phase III TAILORx trial announced at the 2018 ASCO meeting in June suggested that women with a mid-range recurrence score of 11-25 might indeed be able to avoid chemotherapy since it offered no improvement in invasive disease-free survival (iDFS) . Oncotype Dx also helps with evaluating other types of cancers. It is comprised of three tests: 1) Analyses of 21-gene ribonucleic acid signature that could predict the risk of breast cancer recurrence 2) Assay to test 12 genes predicting the risk of recurrence of stage II or III colon cancer and 3) Assay of 17 genes predicting recurrence of prostate cancer .
MammaPrint is another validated genomic predictor test that assays a different set of genes, known as the 70-gene signature (70-GS). Developed by The Netherlands Cancer Institute (NKI) in Amsterdam, MammaPrint received USFDA approval in 2007. It is supported by the highest level of evidence (Level 1A) from the phase III clinical trial, MINDACT . Among all, these two gene panel tests have become trendsetters in the field of precision oncology.
The number of genetic tests prescribed for patients from different medicinal fields has increased manifold over the years. Despite the efforts, individualized therapy has achieved only modest success because of the complexity of such an endeavor. For instance, irrespective of the availability of genomic information, proteome expression levels are harder to predict due to factors such as post-translational modifications (PTMs). Therefore, it is now recognized that the integration of other pan-omics data such as proteomics, metabolomics, and bioinformatics is necessary for making the dream of precision medicine a reality. It would be a mammoth task to make sense of such big data, but it wouldn’t be impossible either.