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Written by Julie Bick, Ph.D.

PGx represents a transformative approach in cancer treatment that utilizes the genetic makeup of both the patient and their cancer to personalize and optimize their cancer therapy, while minimizing adverse effects. It is a crucial component of precision medicine, offering a more targeted process compared to traditional cancer treatments. In this blog, we’ll explore the history, principles, and applications of PGx in oncology, as well as its challenges and future prospects.

Understanding the Role of PGx in Treating Cancer

PGx is a subfield of pharmacology and genetics that studies how genes affect a person’s response to drugs. It focuses on understanding the genetic variations in patients that can influence how they metabolize medications, the efficacy of the drugs, and the likelihood of experiencing significant side effects. By mapping these genetic differences, PGx allows healthcare providers to prescribe medications based on an individual’s genetic profile, and the profile of their specific cancer. One of the earliest breakthroughs in this field came with the identification of the HER2 gene in breast cancer, allowing for targeted therapies like trastuzumab (Herceptin). Today, PGx continues to revolutionize oncology by guiding the use of targeted therapies and immunotherapies, improving patient outcomes and transforming cancer care into a more precise, individualized science.

Fig 1. The incorporation of PGx as the standard-of-care for cancer patients is changing the way we approach cancer. We can now not only identify the most appropriate chemotherapeutic drug, but also fine-tune the dose to minimize toxicity and serious side effects. This strategy is also enabling combination therapies to be deployed earlier in treatment protocols, which is helping reduce the risk of drug-resistance and the re-emergence of cancers in these patients.

The Evolution of Cancer Treatment

Traditionally, cancer treatment followed a “one-size-fits-all” approach, where patients with the same type and stage of cancer received standard treatment protocols. These often include surgery, chemotherapy, radiation therapy, and, more recently, immunotherapy. While these treatments have saved countless lives, they are often accompanied by significant side effects, and they do not work uniformly across all patients. There are multiple reasons for this such as the cancer type and stage; but the patient’s overall health and immune system function, as well as the so-called tumor microenvironment (TME) and the underlying cancer mutations can all highly influence the success of treatment. Although PGx can’t incorporate all of these differences, it can provide a powerful piece of the therapeutic puzzle and help identify the optimal therapy earlier in the treatment journey, which helps to reduce the risk of the development of drug resistance and possible metastases and cancer re-emergence.

Why Chemotherapy Sometimes Fails

For most cancer patients the fear and dread of chemotherapy is one of the first thoughts that accompanies a cancer diagnosis. Chemotherapy works by attacking rapidly dividing cells, but unfortunately, it also affects dividing healthy cells, leading to adverse reactions such as nausea, hair loss, and fatigue. Moreover, not all tumors respond equally to chemotherapy, and some may develop resistance to the drugs, rendering the treatment ineffective. Chemotherapy resistance is achieved a few different ways such as the over expression of proteins called ATP-binding cassette (ABC) transporters that pump chemotherapy drugs out of the cancer cell before they can reach toxic levels, or the direct enzymatic inactivation of the drug before it can damage the cell (Callaghan et. al. 2014). In cases where chemotherapy drugs targeting specific proteins or structures in cancer cells, some of these targets have been shown to mutate enabling the cancer to evade the drug. The most common examples of this include mutations in tubulin, a target of some chemotherapy drugs like taxanes (paclitaxel), or in the enzyme topoisomerase, targeted by drugs like topotecan and doxorubicin (Król et. al. 2010)

Other chemotherapeutics kill cancer cells by causing damage to their DNA and/or trigger apoptosis; however, cancer cells have been shown to enhance their DNA repair mechanisms, fixing the damage before it becomes lethal. For example, BRCA mutations, which impair DNA repair, are targeted by certain drugs (like PARP inhibitors), but cancer cells can sometimes restore DNA repair capacity through additional mutations, and mutations in the p53 gene (a key regulator of apoptosis) can make cancer cells resistant by inhibiting the triggering cell death in response to DNA damage (Baugh et. al. 2017).

Another key aspect to resistance to chemotherapeutic drugs is the TME; this is the environment surrounding cancer cells, which consists of various cell types (immune cells, fibroblasts, endothelial cells, etc.), extracellular matrix (ECM), signaling molecules, and blood vessels. Tumors manipulate this environment to support their growth and evade the immune system. Overcoming the tumor microenvironment is a major challenge in cancer treatments, but recent advances including immune checkpoint inhibitors that work to inhibit the PD-1/PDL-1 and CTLA-4 pathways, are proving to be powerful tools in allowing the immune system to recognize and attack cancer cells.

The improvements in molecular biology and genomic testing are changing the way we approach cancer, both in early- as well as advanced disease where the cancer is often highly heterogeneous, meaning that different cells within a tumor can have distinct genetic profiles and drug-resistance profiles. Cancer is now understood not merely as a disease of uncontrolled cell growth but as a disease of the genome. Mutations in DNA drive the transformation of normal cells into cancerous ones. These mutations are unique to each tumor and can differ even within the same cancer type. As a result, a more individualized approach to treatment has emerged, enabling customization of drug therapies based on genetic factors (Innocenti et. al. 2011).

PGx Strategies for Oncology

Unlike most PGx applications where germline variations are profiled to help assess drug efficacy and toxicity, PGx in oncology involves analyzing both germline and somatic genetic variations in cancer patients. Germline variations are inherited and influence the overall metabolism of drugs, affecting how the body processes various medications. Patients are profiled as Poor Metabolizers through to Ultra Metabolizers, which depending on the medication, may require a dose adjustment up or down (see Table 1). In contrast, somatic variations are acquired mutations found only in cancer cells and can impact how these cells respond to specific treatments, such as targeted therapies and chemotherapy drugs.

Table 1. How PGx- Driven Metabolic Phenotypes Have Clinical Implications Based on the Drug Type.

Metabolic PhenotypeDefinition of Metabolic Profile
Clinical Implication
Active Drug                                                     ProDrug
Ultrarapid Metabolizer (UM)Highly increased enzyme activity compared with normal and rapid metabolizersSignificantly increased enzyme-inactivation of the drug resulting in a reduced drug response and drug efficacySignificantly increased activation of the prodrug, this may lead to serious side-effects and potential toxicity, and response to the drug
Rapid Metabolizer (RM)Higher enzyme activity compared with normal metabolizer, but less than UM.Increased inactivation of the drug resulting in a reduced drug response and reduced efficacyIncreased activation of the pro-drug, this may lead to significant side effects, and increased drug response.
Normal Metabolizer(NM)Fully functional enzyme activityNormal or expected clinical response to the drugNormal or expected clinical response to the prodrug
Intermediate Metabolizer(IM)Decreased enzyme activity compared with normal metabolizers but higher than poor metabolizersReduced inactivation of the drug, leading to increased response and drug-associated side-effectsReduced activation of prodrug resulting in reduced drug response
Poor Metabolizer(PM)Little to no enzyme activitySignificantly reduced inactivation of the drug, resulting in increased drug response and drug-associated side-effects.Significantly reduced activation of the pro-drug, resulting in reduced drug response.

This field has been growing significantly with the growth in biomarker targeting cancer drugs, and as such, the number of drugs with PGx guidelines or drug label requirements is increasing rapidly. Table 2, although not extensive, shows the scale and scope of these PGx initiatives to optimize cancer therapeutic protocols for precision medicine.

Table 2. Summary of Cancer Pharmacogenomic Targets and Their Use to Identify the Right Drug for the Right Cancer Patient.

GeneProteinAssociated CancerKey VariantsTargeted Drugs
ALKAnaplastic Lymphoma (tyrosine) kinaseTo date 16 types of cancer Anaplastic Large Cell Lymphoma (ALCL), Neuroblastoma and NSCLC (Adrian and Paola 2013)F1174 F1245 R1275Ceritinib, Brigatinib, alectinib,
ABL1ABL1 proto-oncogene tyrosine kinase (Philadelphia Chromosome)Chronic Myelocytic Leukemia (CML) Ph+ leukemias (Matthew et. al. 2014)BCR-ABL fusion variants M244, G250, Q252, Y253, E255, V299, F311, T315, F317, M351, F359, and H396Notable- T3151 variant imatinib-resistant CMLImatinib, Dasatinib, Ponatinib
BCR (Philadelphia Chromosome)CMLBCR activator of RhoGEF and GTPaseimatinib, nilotinib, dasatinib, rebastinib, bosutinib, ponatinib all target the ABL-1 protein
BRCA1Tumor suppressor proteinBreast Ovarian (Swensen et. al. 1994)Over 1700 mutations documented BRCA1 185delAG (187delAG most well documentedhttps://research.nhgri.nih.gov/bic/Olaparib, Rucaparib Niraparib Talazoparib
BRAFRAF protein kinaseMalignant melanoma (Menzies et. al. 2021)Most common rs113488022Vemurafenib, Dabrafenib,
EGFREpidermal growth factor receptor- transmembrane glycoprotein/protein kinaseNon-small cell lung cancer (NSCLC), metastatic colorectal cancer (CRC), neck-squamous-cell carcinoma (HNSCC), pancreatic cancer,COSMIC database for EGFR (Forbes et. al. 2010)http://www.sanger.ac.uk/cosmicGefitinib and Erlotinib target EGFR; Afatinib, Lapatinib, Neratinib inhibit EGFR and HER2, Pelitinib inhibits EGFR, HER2 and HER4 and Vandetanib inhibits EGFR, VEGFR and RET-tyrosine kinase.
ERBB2 (HER2)EGRF receptor tyrosine kinaseBreast cancer (Pharmacogenomics at work. Nat Biotechnol 16, 885 (1998).Trastuzumab Pertuzumab, Lapatinib, Neratinib
KITReceptor tyrosine kinaseLeukemias AML, CML Melanoma Gastrointestinal stromal tumors (GIST)Debrafenib, Imatinib, Vermurafenib,
KRASKirsten rat sarcoma viral oncogene.Advanced or metastatic non-small cell lung cancerrs121913530 required for Sotorasib, AdagrasibBRAFTOVI, Tafinlar; Sotorasib, Adagrasib
NRASNeuroblastoma RAS oncogene homologNon-small cell lung cancerrs1065634Carboplatin, Cisplatin, Gemcitabine
ABCB1multi-drug resistance protein 1HIVColorectal neoplasmsRheumatoid arthritisPlatelet reactivityKidney or liver transplantationOpioid- Use -disordersrs1045642rs1045642rs1045642rs1045642rs2032582 rs1128503rs1045642 rs1128503Highly active antiretroviral therapy (HAART) Cisplatin, 5-FUMethotrexateclopidogreltacrolimusfentanyl, morphine. methadone
CYP2D6Cytochrome p450 (highly polymorphic)Cardiac rhythm diseaseDepressionMajor Depressive DisorderPain managementCYP2D6*1, CYP2D6*10CYP2D6*1, CYP2D6*4rs1065852CYP2D6*1, CYP2D6*2, CYP2D6*2xN, CYP2D6*3, CYP2D6*4, CYP2D6*6PropafenoneSSRIsEscitalopramOxycodone
DYPDDihydropyrimidine dehydrogenaseNeoplasmsrs1801160, rs2297595, rs1801266, rs148994843, rs3918290 (dose)rs201268750, rs374527058, rs371258350, rs748620513, rs372307932, rs575853463, rs3918290 (not a complete list of variants) [toxicity]rs17376848rs3918290Fluorouracil.capecitabinetegafur
GSTP1Glutathione S-transferaseNeoplasmsrs1695Cisplatin,fluorouracil,epirubicin,cyclophospham.Doxorubicin,oxaliplatin
MTHFRMethylenetetrahydrofolate reductaseNeoplasmsPrecursor Cell Lymphoblastic Leukemia-LymphomaBurkitt Lymphoma Non-Hodgkin Lymphomars1801133rs1801131Capecitabine, fluorouracil.MercaptopurineMethotrexate
TPMTThiopurine methyltransferaseNeoplasmsTransplantationPrecursor Cell Lymphoblastic Leukemia-Lymphomars1142345, rs12201199, rs1800460,TPMT*1, TPMT*2, TPMT*3A, TPMT*3Crs1142345Cisplatin, cyclophosphamide,AzathioprineMercaptopurine
NUDT15Nudix hydrolaseLeukopeniaNeutropeniaPrecursor Cell Lymphoblastic Leukemia-LymphomaNUDT15*1, NUDT15*4, NUDT15*5, NUDT15*6NUDT15*1, NUDT15*6rs746071566,rs766023281,Mercaptopurine
TYMSThymidylate synthaseRheumatoid arthritis, Psoriasis, Liver diseasePrecursor Cell Lymphoblastic Leukemia-Lymphomars11280056rs45445694Methotrexate
UGT1A1UDP-glucuronosyltransferaseLymphoma NeoplasmsUTG1A1*1, UTG1A1*28,rs4124874Carvedilolirinotecan

Note: not an exhaustive list of variants, shaded green are predictive pharmacogenes, shaded blue are germline pharmacogenes.

By understanding these genetic differences in these key genes, physicians now have a tool to help tailor treatment plans to the individual patient. For instance, patients with certain genetic variants in the DYPD gene may metabolize drugs like fluoropyrimidines or thiopurines differently, requiring adjustments to dosage to avoid therapeutic failure or severe side-effects or even death from the drugs. PGx testing can also predict a patient's response to specific treatments like tamoxifen, which is influenced by CYP2D6 activity, or cisplatin, which has guidelines for TPMT preemptive testing to avoid hearing loss in pediatric patients. The use of PGx profiling to predict how a patient will metabolize a drug, is preventing adverse drug reactions and even save patient’s lives. This is particularly important in oncology, where the toxic effects of chemotherapy and supportive care medications can be severe.

Randomized trials indicate that PGx-guided selection of antidepressants and pain management strategies can improve response and remission rates for cancer patients experiencing depression. (Patel et. al. 2021; Massie 2004) and help optimize main management (Patel et. al. 2019). In this regard, PGx can really improve the quality of a patient’s life when going through cancer. Unfortunately, at this point in time PGx is rarely applied this way, and means that for many cancer patients, their treatment journey is still a difficult one.

Different healthcare systems have varying levels of integration of PGx guidelines, which can lead to inconsistent applications and hinder their adoption in routine practice. Implementing PGx testing requires changes in clinical workflows, additional resources, and efficient data management systems. Moreover, uncertainty about insurance coverage for PGx testing can limit its accessibility, especially in regions without clear reimbursement policies​

Future Directions

The growth in the adoption of PGx as the standard-of-care in cancer treatment is supporting precision solutions, more effective care, and most importantly providing hope to patients with cancer.  Ongoing advancements in next-generation sequencing (NGS) and other diagnostic platforms that allow for a more detailed understanding of both germline and somatic variations will only further expand this field of precision medicine. Regulatory agencies, including the FDA and EMA, are also increasingly incorporating PGx information in drug labels, guiding clinicians on how to use genetic information in treatment decisions​.

While genetic testing has become more accessible in recent years, it is still not universally available. The cost of genomic sequencing can be prohibitive for some patients, and insurance coverage for these tests varies. Additionally, access to testing may be limited in certain geographic regions or healthcare settings. However, the tide is slowly turning, and physicians, healthcare providers and insurers all see benefits to the integration of PGx into clinical decisions for cancer. With the increasing incidence of cancers across the globe, there is a growing need not only for targeted therapies, combination therapies and immunomodulation strategies to provide the best survival outcomes, but also the need for careful attention to quality of life for cancer patients- and PGx is a simple solution to address both sides of these treatment needs.

References

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Matthew, S., et. al.  BCR-ABL1 compound mutations combining key kinase domain positions confer clinical resistance to ponatinib in Ph chromosome-positive leukemia. Cancer cell. 2014. PMID:25132497;  PMCID:PMC4160372;  DOI:10.1016/j.ccr.2014.07.006

Swensen, J., et. al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science (New York, N.Y.). 1994. Oct 7;266(5182):66-71. doi: 10.1126/science.7545954

Menzies et. al. Distinguishing Clinicopathologic Features of Patients with V600E and V600K BRAF-Mutant Metastatic Melanoma. Clin Cancer Res (2012) 18 (12): 3242–3249.

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Patel, J.N et. al. Pharmacogenetic (PGx) guided cancer pain management in an oncology palliative medicine (PM) clinic. 2019 Journal of Clinical Oncology Vol. 37, Number 31_suppl

Patel, J.N. Opportunities for pharmacogenomics-guided supportive care in cancer. Support Care Cancer 29, 555–557 (2021).

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