Pharmacogenetics of cancer drugs

Innovation in cancer pharmacotherapy through integrative consideration of germline and tumor genomes

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or are in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, for example, somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for >5 decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information, which derives from both the tumor and the germline genome, and taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to integrate PGx data in cancer therapy with a special meaning for interdisciplinary molecular tumor boards, in which cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. SIGNIFICANCE STATEMENT: The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, the full potential of targeted therapy remains untapped owing to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Unlocking the potential of Molecular Tumor Boards: from cutting-edge data interpretation to innovative clinical pathways

The emerging era of precision medicine is characterized by an increasing availability of targeted anticancer therapies and by the parallel development of techniques to obtain more refined molecular data, whose interpretation may not always be straightforward. Molecular tumor boards gather various professional figures, in order to leverage the analysis of molecular data and provide prognostic and predictive insights for clinicians. In addition to healthcare development, they could also become a tool to promote knowledge and research spreading. A growing body of evidence on the application of molecular tumor boards to clinical practice is forming and positive signals are emerging, although a certain degree of heterogeneity exists. This work analyzes molecular tumor boards' potential workflows, figures involved, data sources, sample matrices and eligible patients, as well as available evidence and learning examples. The emerging concept of multi-institutional, disease-specific molecular tumor boards is also considered by presenting two ongoing nationwide experiences.

Synergistic effects of immune checkpoints and checkpoint inhibitors in inflammatory neuropathies: Implications and mechanisms

Immune checkpoint molecules play pivotal roles in the regulation of immune homeostasis. Disruption of the immune checkpoints causes autoimmune/inflammatory as well as malignant disorders. Over the past few years, the immune checkpoint molecules with inhibitory function emerged as potential therapeutic targets in oncological conditions. The inhibition of the function of these molecules by using immune checkpoint inhibitors (ICIs) has brought paradigmatic changes in cancer therapy due to their remarkable clinical benefits, not only in improving the quality of life but also in prolonging the survival time of cancer patients. Unfortunately, the ICIs soon turned out to be a "double-edged sword" as the use of ICIs caused multiple immune-related adverse effects (irAEs). The development of inflammatory neuropathies such as Guillain-Barré syndrome (GBS) and Chronic Inflammatory Demyelinating Polyradiculoneuropathy (CIDP) as the secondary effects of immunotherapy appeared very challenging as these conditions result in significant and often permanent disability. The underlying mechanism(s) through which ICIs trigger inflammatory neuropathies are currently not known. Compelling evidence suggests autoimmune reaction and/or inflammation as the independent risk mechanism of inflammatory neuropathies. There is a lack of understanding as to whether prior exposure to the risk factors of inflammatory neuropathies, the presence of germline genetic variants in immune function-related genes, genetic variations within immune checkpoint molecules, the existence of autoantibodies, and activated/memory T cells act as determining factors for ICI-induced inflammatory neuropathies. Herein, we highlight the available pieces of evidence, discuss the mechanistic basis, and propose a few testable hypotheses on inflammatory neuropathies as irAEs of immunotherapy.

Evolution of predictive risk factor analysis for chemotherapy-related toxicity

The causes of variation in toxicity to the same treatment regimen among seemingly similar patients remain largely unknown. There was tremendous optimism that the patient's germline genome would be strongly predictive of treatment-related toxicity and could be used to personalize treatment and improve therapeutic outcomes. However, there has been limited success in discovering robust pharmacogenetic predictors of treatment-related toxicity and even less progress in translating the few validated predictors into clinical practice. It is apparent that identification of toxicity predictors that can be used to predict and prevent treatment-related toxicity will require thinking beyond germline genomics. To that end, we propose an integrated biomarker discovery approach that recognizes that a patient's toxicity risk is determined by the cumulative effects of a broad range of "omic" and non-omic factors. This commentary describes the limited success in discovering and translating clinical and pharmacogenetic toxicity predictors into clinical practice. We illustrate the evolution of cancer toxicity biomarker discovery and translation through studies of taxane-induced peripheral neuropathy, which is one of the most common and debilitating side effects of cancer treatment. We then discuss the opportunities for discovering non-genomic (e.g., metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarkers that may be more strongly predictive of toxicity risk and the potential challenges with translating integrated biomarkers into clinical practice. This integrated biomarker discovery approach may circumvent some of the major limitations in toxicity biomarker science and move precision oncology treatment forward so that patients receive maximum treatment benefit with minimal toxicity.

IL-7 germline variant: setting the stage for immune-related adverse events

Treatment with immune checkpoint inhibitors (ICIs) is frequently associated with immune-related adverse events (irAEs). A new study identified an interleukin 7 (IL-7) allelic variant-rs16906115-as a major risk factor for the development of ICI-associated irAEs. This association is of great significance as it indicates that germline genetic variants influence the occurrence of irAEs, thus opening a new avenue for identifying high-risk patients to enable better management of ICI therapy and associated irAEs.

Immunogenetic variations predict immune-related adverse events for PD-1/PD-L1 inhibitors

BACKGROUND

PD-1/PD-L1 inhibitors have brought remarkable benefits but can cause profound immune-related adverse events (irAEs). The host immunogenetic background is likely to play a role in irAE susceptibility. In this study, we aimed to identify potential immunogenetic biomarkers to predict irAEs.

METHODS

Patients with solid tumours receiving PD-1/PD-L1 blockade were recruited and followed up. Genes considered pivotal contributors to tumour-immunity and autoimmune diseases were screened out via protein-protein interaction network and Cytoscape. Consequently, thirty-nine variants in eighteen genes were genotyped using the multiplex genotyping assay. Association analysis between genetic variants and irAEs as well as irAEs-free survival was performed.

RESULTS

Four immunogenetic variants as predictive biomarkers of irAEs were identified. The C allele of Mitogen-Activated Protein Kinase 1 (MAPK1) rs3810610 (odds ratio [OR] = 1.495, 95% confidence interval [CI] = 1.093-2.044, P = 0.012) was a risk predictor while the A allele of PTPRC rs6428474 (OR = 0.717, 95% CI = 0.521-0.987, P = 0.041) was a protective factor for all-grade irAEs. The A allele of ADAD1 rs17388568 (OR = 2.599, 95% CI = 1.355-4.983, P = 0.003) increased the risk while the G allele of IL6 rs1800796 (OR = 0.425, 95% CI = 0.205-0.881, P = 0.018) protected patients from high-grade irAEs. Significant immunogenetic variants reached a similar tendency in PD-1 blockade or lung cancer subgroups. In multivariate Cox regression analysis, the MAPK1 rs3810610 was an independent factor regarding all-grade irAEs-free survival (CC versus CT or TT: hazard ratio [HR] = 0.71, 95% CI = 0.52-0.99, P = 0.042). ADAD1 rs17388568 (AA versus AG or GG: HR = 0.11, 95% CI = 0.025-0.49, P = 0.004) and IL6 rs1800796 (GG or GC versus CC: HR = 3.10, 95% CI = 1.315-7.29, P = 0.01) were independent variables for high-grade irAEs-free survival.

CONCLUSION

We first identified several immunogenetic polymorphisms associated with irAEs and irAEs-free survival in PD-1/PD-L1 blockade-treated tumour patients, and they may serve as potential predictive biomarkers.

Germline genetic variation and predicting immune checkpoint inhibitor induced toxicity

Immune checkpoint inhibitor (ICI) therapy has revolutionised the treatment of various cancer types. ICIs reinstate T-cell function to elicit an anti-cancer immune response. The resulting immune response can however have off-target effects which manifest as autoimmune type serious immune-related adverse events (irAE) in ~10-55% of patients treated. It is currently challenging to predict both who will experience irAEs and to what severity. Identification of patients at high risk of serious irAE would revolutionise patient care. While the pathogenesis driving irAE development is still unclear, host genetic factors are proposed to be key determinants of these events. This review presents current evidence supporting the role of the host genome in determining risk of irAE. We summarise the spectrum and timing of irAEs following treatment with ICIs and describe currently reported germline genetic variation associated with expression of immuno-modulatory factors within the cancer immunity cycle, development of autoimmune disease and irAE occurrence. We propose that germline genetic determinants of host immune function and autoimmune diseases could also explain risk of irAE development. We also endorse genome-wide association studies of patients being treated with ICIs to identify genetic variants that can be used in polygenic risk scores to predict risk of irAE.

Pharmacogenomic Biomarkers

Why does the usual dose of medication work for a person while another individual cannot give the expected response to the same drug? On the other hand, how come half of the usual dose of an analgesic relieves an individual’s pain immediately, as another man continue to suffer even after taking double dose? Although a treatment method has been successfully used in majority of the population for many years, why does the same therapy cause serious side effects in another region of the world? Most presently approved therapies are not effective in all patients. For example, 20-40% of patients with depression respond poorly or not at all to antidepressant drug therapy. Many patients are resistant to the effects of antiasthmatics and antiulcer drugs or drug treatment of hyperlipidemia and many other diseases. The reason for all those is basically interindividual differences in genomic structures of people, which are explained in this chapter in terms of the systems and the most frequently used drugs in clinical treatment.

Safety, Feasibility, and Merits of Longitudinal Molecular Testing of Multiple Metastatic Sites to Inform mTNBC Patient Treatment in the Intensive Trial of Omics in Cancer

Patients with metastatic triple-negative breast cancer (mTNBC) have poor outcomes. The Intensive Trial of Omics in Cancer (ITOMIC) sought to determine the feasibility and potential efficacy of informing treatment decisions through multiple biopsies of mTNBC deposits longitudinally over time, accompanied by analysis using a distributed network of experts.

In the Intensive Trial of Omics in Cancer (ITOMIC), the feasibility and potential efficacy of informing treatment decisions through omics analysis of multiple biopsies of mTNBC deposits over time was assessed. An ITOMIC Tumor Board (ITB) that comprised experts discussed tumor profile findings and made treatment recommendations to each subject's physician. Study-directed omics analysis revealed that of the 31 enrolled subjects, two were found to have lung cancer, one a carcinoma of unknown primary site that and tumor samples from five subjects showed some receptor-positivity. Several subjects survived well beyond what would be expected for this patient group, supporting the merits of further investigation of this approach.

Immune Checkpoint Inhibitor-Induced Central Diabetes Insipidus: Looking for the Needle in the Haystack or a Very Rare Side-Effect to Promptly Diagnose?

Immune checkpoint inhibitors have improved the survival in patients affected by an increasing number of malignancies, but they may also trigger various autoimmune side-effects, including endocrinopathies. Very rarely, immune checkpoint inhibitors have been reported to cause central diabetes insipidus. However, with their expanding use, the likelihood that oncologists will face this endocrine adverse event is expected to increase. By reviewing the limited literature on central diabetes insipidus induced by immune checkpoint inhibitors, some inconsistencies emerge in the diagnosis and the management of patients presenting with this toxicity, together with difficulties related to classifying its severity. Until now, specific guidelines on the management of central diabetes insipidus induced by immune checkpoint inhibitors are lacking. In clinical practice, endocrinological consultation may relieve medical oncologists from difficulties in treating this side-effect; oncologists, however, remain responsible for its early diagnose and the management of the causative drugs. To this aim, some practical suggestions are advised for the multidisciplinary management of cancer patients presenting with central diabetes insipidus induced by immune checkpoint inhibitors.

The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients

Many anticancer drugs cause adverse drug reactions (ADRs) that negatively impact safety and reduce quality of life. The typical narrow therapeutic range and exposure-response relationships described for anticancer drugs make precision dosing critical to ensure safe and effective drug exposure. Germline mutations in pharmacogenes contribute to inter-patient variability in pharmacokinetics and pharmacodynamics of anticancer drugs. Patients carrying reduced-activity or loss-of-function alleles are at increased risk for ADRs. Pretreatment genotyping offers a proactive approach to identify these high-risk patients, administer an individualized dose, and minimize the risk of ADRs. In the field of oncology, the most well-studied gene-drug pairs for which pharmacogenetic dosing recommendations have been published to improve safety are DPYD-fluoropyrimidines, TPMT/NUDT15-thiopurines, and UGT1A1-irinotecan. Despite the presence of these guidelines, the scientific evidence showing the benefits of pharmacogenetic testing (e.g., improved safety and cost-effectiveness) and the development of efficient multi-gene genotyping panels, routine pretreatment testing for these gene-drug pairs has not been implemented widely in the clinic. Important considerations required for widespread clinical implementation include pharmacogenetic education of physicians, availability or allocation of institutional resources to build an efficient clinical infrastructure, international standardization of guidelines, uniform adoption of guidelines by regulatory agencies leading to genotyping requirements in drug labels, and development of cohesive reimbursement policies for pretreatment genotyping. Without clinical implementation, the potential of pharmacogenetics to improve patient safety remains unfulfilled.

Immune Checkpoint Inhibitors as a Threat to the Hypothalamus-Pituitary Axis: A Completed Puzzle

Immune checkpoint inhibitors (ICI) prolong the survival in an increasing number of patients affected by several malignancies, but at the cost of new toxicities related to their mechanisms of action, autoimmunity. Endocrine toxicity frequently occurs in patients on ICI, but endocrine dysfunctions differ based on the ICI-subclass, as follows: agents targeting the CTLA4-receptor often induce hypophysitis and rarely thyroid dysfunction, which is the opposite for agents targeting the PD-1/PD-L1 axis. Recently, few cases of central diabetes insipidus have been reported as an adverse event induced by both ICI-subclasses, either in the context of anterior hypophysitis or as selective damage to the posterior pituitary or in the context of hypothalamitis. These new occurrences demonstrate, for the first time, that ICI-induced autoimmunity may involve any tract of the hypothalamic-pituitary axis. However, the related pathogenic mechanisms remain to be fully elucidated. Similarly, the data explaining the endocrine system susceptibility to primary and ICI-induced autoimmunity are still scarce. Since ICI clinical indications are expected to expand in the near future, ICI-induced autoimmunity to the hypothalamic-pituitary axis presents as a unique in vivo model that could help to clarify the pathogenic mechanisms underlying both the dysfunction induced by ICI to the hypothalamus-pituitary axis and primary autoimmune diseases affecting the same axis.

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability

PURPOSE

Paclitaxel is a commonly used chemotherapy drug with substantial variability in pharmacokinetics (PK) that affects treatment efficacy and toxicity. Pharmacometabolomic signatures that explain PK variability could be used to individualize dosing to improve therapeutic outcomes. The objective of this study was to identify pretreatment metabolites or metabolomic signatures that explain variability in paclitaxel PK.

METHODS

This analysis was conducted using data previously collected on a prospective observational study of 48 patients with breast cancer receiving weekly 80 mg/m² paclitaxel infusions. Paclitaxel plasma concentrations were measured during the first infusion to estimate paclitaxel time above threshold (T_c>0.05) and maximum concentration (C_max). Metabolites measured in pretreatment whole blood by nuclear magnetic resonance spectrometry were analyzed for an association with T_c>0.05 and C_max using Pearson correlation followed by stepwise linear regression.

RESULTS

Pretreatment creatinine, glucose, and lysine concentrations were positively correlated with T_c>0.05, while pretreatment betaine was negatively correlated and lactate was positively correlated with C_max (all uncorrected p < 0.05). After stepwise elimination, creatinine was associated with T_c>0.05, while betaine and lactate were associated with C_max (all p < 0.05).

CONCLUSION

This study identified pretreatment metabolites that may be associated with paclitaxel PK variability demonstrating feasibility of a pharmacometabolomics approach for understanding paclitaxel PK. However, identification of more robust pharmacometabolomic predictors will be required for broad and routine application for the clinical dosing of paclitaxel.

Case Report: Ipilimumab-Induced Panhypophysitis: An Infrequent Occurrence and Literature Review

Background

Immune checkpoint inhibitors (ICIs), by unleashing the anticancer response of the immune system, can improve survival of patients affected by several malignancies, but may trigger a broad spectrum of adverse events, including autoimmune hypophysitis. ICI-related hypophysitis mainly manifests with anterior hypopituitarism, while the simultaneous involvement of both anterior and posterior pituitary (i.e., panhypophysitis) has rarely been described.

Case Presentation

In June 2015, a 64-year-old man affected by liver metastases of a uveal melanoma was referred to us due to polyuria and polydipsia. Two months prior, he had started ipilimumab therapy (3 mg/kg iv every 21 days). The treatment was well-tolerated (only mild asthenia and diarrhea were reported). A few days before the fourth cycle, the patient complained of intense headaches, profound fatigue, nocturia, polyuria (up to 10 L urine/daily), and polydipsia. Laboratory tests were consistent with adrenal insufficiency, hypothyroidism, and transient central diabetes insipidus. The pituitary MRI showed an enlarged gland with microinfarcts, while the hypophyseal stalk was normal, and the neurohypophyseal 'bright signal' in T1 sequences was not detected. The treatment included dexamethasone (then cortisone acetate at replacement dose), desmopressin, and levothyroxine. Within the next five days, the symptoms resolved, and blood pressure, electrolytes, glucose, and urinalysis were stable within the normal ranges; desmopressin was discontinued while cortisone acetate and levothyroxine were maintained. The fourth ipilimumab dose was entirely administered in the absence of further side effects.

Conclusion

As ICIs are increasingly used as anticancer agents, the damage to anterior and/or posterior pituitary can be progressively encountered by oncologists and endocrinologists in their clinical practice. Patients on ICIs and their caregivers should be informed about that risk and be empowered to alert the referring specialists early, at the onset of panhypopituitarism symptoms, including polyuria/polydipsia.

VEGF-Related Germinal Polymorphisms May Identify a Subgroup of Breast Cancer Patients with Favorable Outcome under Bevacizumab-Based Therapy-A Message from COMET, a French Unicancer Multicentric Study

The prospective multicenter COMET trial followed a cohort of 306 consecutive metastatic breast cancer patients receiving bevacizumab and paclitaxel as first-line chemotherapy. This study was intended to identify and validate reliable biomarkers to better predict bevacizumab treatment outcomes and allow for a more personalized use of this antiangiogenic agent. To that end, we aimed to establish risk scores for survival prognosis dichotomization based on classic clinico-pathological criteria combined or not with single nucleotide polymorphisms (SNPs). The genomic DNA of 306 patients was extracted and a panel of 13 SNPs, covering seven genes previously documented to be potentially involved in drug response, were analyzed by means of high-throughput genotyping. In receiver operating characteristic (ROC) analyses, the hazard model based on a triple-negative cancer phenotype variable, combined with specific SNPs in VEGFA (rs833061), VEGFR1 (rs9582036) and VEGFR2 (rs1870377), had the highest predictive value. The overall survival hazard ratio of patients assigned to the poor prognosis group based on this model was 3.21 (95% CI (2.33-4.42); p < 0.001). We propose that combining this pharmacogenetic approach with classical clinico-pathological characteristics could markedly improve clinical decision-making for breast cancer patients receiving bevacizumab-based therapy.

Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy

Precision medicine requires accurate technologies for drug administration and proper systems pharmacology approaches for patient data analysis. Here, plasma pharmacokinetics (PK) data of the OPTILIV trial in which cancer patients received oxaliplatin, 5-fluorouracil and irinotecan via chronomodulated schedules delivered by an infusion pump into the hepatic artery were mathematically investigated. A pump-to-patient model was designed in order to accurately represent the drug solution dynamics from the pump to the patient blood. It was connected to semi-mechanistic PK models to analyse inter-patient variability in PK parameters. Large time delays of up to 1h41 between the actual pump start and the time of drug detection in patient blood was predicted by the model and confirmed by PK data. Sudden delivery spike in the patient artery due to glucose rinse after drug administration accounted for up to 10.7% of the total drug dose. New model-guided delivery profiles were designed to precisely lead to the drug exposure intended by clinicians. Next, the complete mathematical framework achieved a very good fit to individual time-concentration PK profiles and concluded that inter-subject differences in PK parameters was the lowest for irinotecan, intermediate for oxaliplatin and the largest for 5-fluorouracil. Clustering patients according to their PK parameter values revealed patient subgroups for each drug in which inter-patient variability was largely decreased compared to that in the total population. This study provides a complete mathematical framework to optimize drug infusion pumps and inform on inter-patient PK variability, a step towards precise and personalized cancer chronotherapy.

Accuracy and safety of infusion pumps remain a critical issue in the clinics and the development of accurate mathematical models to optimize drug administration though such devices has a key part to play in the advancement of precision medicine. Here, PK data from cancer patient receiving irinotecan, oxaliplatin and 5-fluorouracil into the hepatic artery via an infusion pump was mathematically investigated. A pump-to-patient model was designed and revealed significant inconsistencies between intended drug profiles and actual plasma concentrations. This mathematical model was then used to suggest improved profiles in order to minimise error and optimise delivery. Physiologically-based PK models of the three drugs were then linked to the pump-to-patient model. The whole framework achieved a very good fit to data and allowed quantifying inter-patient variability in PK parameters and linking them to potential clinical biomarkers via patient clustering. The developed methodology improves our understanding of patient-specific drug pharmacokinetics towards personalized drug administration.

Pharmacogenetics in the clinical analysis laboratory: clinical practice, research, and drug development pipeline

Introduction: Over the last decade, the spread of next-generation sequencing technology along with the rising cost in health management in national health systems has led to widespread use/abuse of pharmacogenetic tests (PGx) in the practice of many clinical disciplines. However, given their clinical significance, it is important to standardize these tests for having an interaction with the clinical analysis laboratory (CAL), in which a PGx service can meet these requirements. Areas covered: A diagnostic test must meet the criteria of reproducibility and validity for its utility in the clinical routine. This present review mainly describes the utility of introducing PGx tests in the CAL routine to produce correct results useful for setting up personalized drug treatments. Expert opinion: With a PGx service, CALs can provide the right tool to help clinicians to make better choices about different categories of drugs and their dosage and to manage the economic impact both in hospital-based settings and in National Health Services, throughout electronic health records. Advances in PGx also allow a new approach for pharmaceutical companies in order to improve drug development and clinical trials. As a result, CALs can achieve a powerful source of epidemiological, clinical, and research findings from PGx tests.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

BACKGROUND

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors.

RESULTS

We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies.

CONCLUSIONS

Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Early disease relapse in a patient with colorectal cancer who harbors genetic variants of DPYD, TYMS, MTHFR and DHFR after treatment with 5-fluorouracil-based chemotherapy

Background Early relapse in colorectal cancer (CRC) after curative resection is mainly attributed to the key determinants such as tumor histology, stage, lymphovascular invasion, and the response to chemotherapy. Case presentation Interindividual variability in the efficacy of adjuvant chemotherapy between patients receiving the same treatment may be ascribed to the patients' genetic profile. In this report, we highlight a clinical case of a patient with stage II CRC who relapsed within a short period after starting adjuvant chemotherapy and was later found to have multiple genetic polymorphisms in the DPYD, TYMS, MTHFR, and DHFR genes. Conclusions Based on the clinical data of the patient and the key role of these genes in 5-fluorouracil pathway, we hypothesize that these variants may contribute to the drug response and early relapse in CRC.

Clinical Pharmacogenetics for Precision Medicine: Successes and Setbacks

BACKGROUND

Pharmacogenetics is a key component in the delivery of therapeutics to maximize pharmacologic efficacy and minimize toxicity. There are numerous identified gene-drug pairs that demonstrate the utility of pharmacogenetics testing for drug or dose selection. Although some of these pairs have translated into clinical use, pharmacogenetic testing has not yet made its way into routine clinical practice at many institutions.

CONTENT

This review provides an overview of clinically actionable pharmacogenetics in precision medicine. Examples of successfully implemented gene-drug pairs, along with common testing methodologies and guidelines for application, are discussed. Remaining barriers to widespread clinical implementation are also examined.

SUMMARY

There is a recognized role for genotyping in the guidance of therapeutic drug regimens and the prevention of adverse drug reactions. Evidence-based guidelines are available to aid in the selection of treatment upon pharmacogenetics testing for established gene-drug pairs. Multidisciplinary clinical collaboration and clinical decision support tools will be critical for widespread adoption, and financial reimbursement barriers remain.

Association between single-nucleotide polymorphisms and adverse events in nivolumab-treated non-small cell lung cancer patients

Background

Treatment with PD-1 inhibitors can be hampered by severe auto-immune-related toxicities. Our objective was to identify single-nucleotide polymorphisms (SNPs) in genes previously associated with auto-immunity, which are associated with toxicities in nivolumab-treated NSCLC patients. This was in order to identify patients prone to develop severe toxicities and to gain more insight into the underlying pathobiology.

Methods

We analysed 322 nivolumab-treated patients and assessed the association with toxicities for seven SNPs in four genes, which are considered contributors to PD-1-directed T-cell responses, i.e., PDCD1, PTPN11, ZAP70 and IFNG. Every SNP was tested for its association with toxicity endpoints. Significant associations were tested in a validation cohort.

Results

A multivariable analysis in the exploration cohort showed that homozygous variant patients for PDCD1 804C>T (rs2227981) had decreased odds for any grade treatment-related toxicities (n = 96; OR 0.4; 95% CI 0.2–1.0; p = 0.039). However, this result could not be validated (n = 85; OR 0.9; 95% CI 0.4–1.9; p = NS).

Conclusions

Our results show that it is unlikely that the investigated SNPs have a clinical implication in predicting toxicity. A finding, even though negative, that is considered timely and instructive towards further research in biomarker development for checkpoint inhibitor treatments.

Influence of the ABCB1 polymorphisms on the response to Taxane-containing chemotherapy: a systematic review and meta-analysis

PURPOSE

Multidrug resistance mediated by ABCB1 has been perceived to be one of the obstacles for cancer chemotherapy. This meta-analysis was performed to verify the effect of the ABCB1 rs1045642 and rs1128503 polymorphisms on the response to Taxane-containing chemotherapy.

METHODS

Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were employed to evaluate the impact of these two ABCB1 polymorphisms. R scripts were developed to perform the meta-analysis.

RESULTS

A total of nine articles (including nine studies for rs1045642 and five for rs1128503) were collected in our systematic review. However, our meta-analysis showed no significant effect of these two ABCB1 polymorphisms on the response to Taxane-containing regimens.

CONCLUSIONS

This study highlights the unsuitability of relying on the ABCB1 rs1045642 and rs1128503 polymorphisms as therapeutic response biomarkers of Taxane-containing chemotherapy. Further polycentric studies in larger and multiracial populations are needed to validate the conclusions.

Precision medicine and pharmacogenetics: what does oncology have that addiction medicine does not?

BACKGROUND AND AIMS

Precision, personalized or stratified medicine, which promises to deliver the right treatment to the right patient, is a topic of international interest in both the lay press and the scientific literature. A key aspect of precision medicine is the identification of biomarkers that predict the response to medications (i.e. pharmacogenetics). We examined why, despite the great strides that have been made in biomarker identification in many areas of medicine, only in oncology has there been substantial progress in their clinical implementation. We also considered why progress in this effort has lagged in addiction medicine.

METHODS

We compared the development of pharmacogenetic biomarkers in oncology, cardiovascular medicine (where developments are also promising) and addictive disorders.

RESULTS

The first major reason for the success of oncologic pharmacogenetics is ready access to tumor tissue, which allows in-vitro testing and insights into cancer biology. The second major reason is funding, with cancer research receiving, by far, the largest allocation by the National Institutes of Health (NIH) during the past two decades. The second largest allocation of research funding has gone to cardiovascular disease research. Addictions research received a much smaller NIH funding allocation, despite the major impact that tobacco use, alcohol consumption and illicit drug use have on the public health and healthcare costs.

CONCLUSIONS

Greater support for research on the personalized treatment of addictive disorders can be expected to yield disproportionately large benefits to the public health and substantial reductions in healthcare costs.

Pharmacogenetic determinants of outcomes on triplet hepatic artery infusion and intravenous cetuximab for liver metastases from colorectal cancer (European trial OPTILIV, NCT00852228)

Background:

The hepatic artery infusion (HAI) of irinotecan, oxaliplatin and 5-fluorouracil with intravenous cetuximab achieved outstanding efficacy in previously treated patients with initially unresectable liver metastases from colorectal cancer. This planned study aimed at the identification of pharmacogenetic predictors of outcomes.

Methods:

Circulating mononuclear cells were analysed for 207 single-nucleotide polymorphisms (SNPs) from 34 pharmacology genes. Single-nucleotide polymorphisms passing stringent Hardy–Weinberg equilibrium test were tested for their association with outcomes in 52 patients (male/female, 36/16; WHO PS, 0–1).

Results:

VKORC1 SNPs (rs9923231 and rs9934438) were associated with early and objective responses, and survival. For rs9923231, T/T achieved more early responses than C/T (50% vs 5%, P=0.029) and greatest 4-year survival (46% vs 0%, P=0.006). N-acetyltransferase-2 (rs1041983 and rs1801280) were associated with up to seven-fold more macroscopically complete hepatectomies. Progression-free survival was largest in ABCB1 rs1045642 T/T (P=0.026) and rs2032582 T/T (P=0.035). Associations were found between toxicities and gene variants (P<0.05), including neutropenia with ABCB1 (rs1045642) and SLC0B3 (rs4149117 and rs7311358); and diarrhoea with CYP2C9 (rs1057910), CYP2C19 (rs3758581), UGT1A6 (rs4124874) and SLC22A1 (rs72552763).

Conclusion:

VKORC1, NAT2 and ABCB1 variants predicted for HAI efficacy. Pharmacogenetics could guide the personalisation of liver-targeted medico-surgical therapies.

The impact of DNA damage response gene polymorphisms on therapeutic outcomes in late stage ovarian cancer

Late stage epithelial ovarian cancer has a dismal prognosis. Identification of pharmacogenomic markers (i.e. polymorphisms) to stratify patients to optimize individual therapy is of paramount importance. We here report the retrospective analysis of polymorphisms in 5 genes (ATM, ATR, Chk1, Chk2 and CDK12) involved in the cellular response to platinum in a cohort of 240 cancer patients with late stage ovarian cancer. The aim of the present study was to evaluate associations between the above mentioned SNPs and patients’ clinical outcomes: overall survival (OS) and progression free survival (PFS). None of the ATM, ATR, Chk1 and Chk2 polymorphisms was found to significantly affect OS nor PFS in this cohort of patients. Genotype G/G of CDK12 polymorphism (rs1054488) predicted worse OS and PFS than the genotype A/A-A/G in univariate analysis. The predictive value was lost in the multivariate analysis. The positive correlation observed between this polymorphism and age, grade and residual tumor may explain why the CDK12 variant was not confirmed as an independent prognostic factor in multivariate analysis.The importance of CDK12 polymorphism as possible prognostic biomarker need to be confirmed in larger ovarian cancer cohorts, and possibly in other cancer population responsive to platinum agents.

Cancer Precision Medicine: From Cancer Screening to Drug Selection and Personalized Immunotherapy

With the accelerating progress in basic and clinical cancer research, a huge body of new discoveries and powerful technologies has allowed us to implement a 'Cancer Precision Medicine (CPM)' system for cancer patients. The CPM system covers a wide range of cancer management including cancer screening, monitoring of relapse/recurrence, selection/prediction of effective drugs/treatments, and personalized immunotherapy. In this system individual cancer patients expect to receive personalized care: an appropriate dose of the right drug at the right time. We here aim to summarize and discuss a possible workflow for precision medicine for cancer patients by reviewing recent booming technologies and treatments that have been used or will potentially be used in the CPM system.

Pharmacogenetics of Parkinson's Disease in Clinical Practice

Background

Pharmacogenetics aims to identify the genetic factors participating in the heterogeneity of drug response. The ultimate goal is to provide personalized treatment by identifying responders and non-responders, individuals at risk of developing drug adverse effects, and by adjusting dosage. Several studies have been performed in Parkinson's disease (PD), to investigate drug response variability according to genetic factors for dopamine replacement therapies.

Methods

We performed a systematic literature search of articles related to pharmacogenetic studies in PD, and found 47 studies.

Findings

Motor response and adverse reactions to dopaminergic drugs were associated with genes encoding enzymes of their metabolism as well as their receptors or targets. Despite some interesting results, considerable work remains to be done to replicate and validate their clinical relevance before translation into clinical practice.

Conclusions

There are currently no guidelines published for pharmacogenetic factors related to PD drugs. More research is need in this field in order to improve our knowledge in drug response variability in PD. Algorithms taking into account clinical, pharmacological, and genetic factors are probably the most promising way to help for a personalized medicine in PD.

The Importance of Patient-Specific Factors for Hepatic Drug Response and Toxicity

Responses to drugs and pharmacological treatments differ considerably between individuals. Importantly, only 50%-75% of patients have been shown to react adequately to pharmacological interventions, whereas the others experience either a lack of efficacy or suffer from adverse events. The liver is of central importance in the metabolism of most drugs. Because of this exposed status, hepatotoxicity is amongst the most common adverse drug reactions and hepatic liabilities are the most prevalent reason for the termination of development programs of novel drug candidates. In recent years, more and more factors were unveiled that shape hepatic drug responses and thus underlie the observed inter-individual variability. In this review, we provide a comprehensive overview of different principle mechanisms of drug hepatotoxicity and illustrate how patient-specific factors, such as genetic, physiological and environmental factors, can shape drug responses. Furthermore, we highlight other parameters, such as concomitantly prescribed medications or liver diseases and how they modulate drug toxicity, pharmacokinetics and dynamics. Finally, we discuss recent progress in the field of in vitro toxicity models and evaluate their utility in reflecting patient-specific factors to study inter-individual differences in drug response and toxicity, as this understanding is necessary to pave the way for a patient-adjusted medicine.

The pharmacogenomics of drug resistance to protein kinase inhibitors

Dysregulation of growth factor cell signaling is a major driver of most human cancers. This has led to development of numerous drugs targeting protein kinases, with demonstrated efficacy in the treatment of a wide spectrum of cancers. Despite their high initial response rates and survival benefits, the majority of patients eventually develop resistance to these targeted therapies. This review article discusses examples of established mechanisms of drug resistance to anticancer therapies, including drug target mutations or gene amplifications, emergence of alternate signaling pathways, and pharmacokinetic variation. This reveals a role for pharmacogenomic analysis to identify and monitor for resistance, with possible therapeutic strategies to combat chemoresistance.

The role of pharmacogenetics in the treatment of osteosarcoma

In osteosarcoma, large variation is observed in the efficacy and toxicity of chemotherapeutic drugs among similarly treated patients. Treatment optimization using predictive factors or algorithms is of importance, because there has been a lack of improvement of treatment outcome and survival for decades. The outcome of cancer treatment is influenced by the genome, thus studying genetic variants involved in the efficacy and toxicity of the chemotherapeutic drugs used in the treatment of osteosarcoma could be an opportunity to optimize current treatments and improve our understanding of the individual's drug response in osteosarcoma patients. This review discusses the current insights in the pharmacogenetics of the treatment response of osteosarcoma patients regarding efficacy and toxicity, and implications for future research and treatment.

Functional Analysis of SNPs in the ERCC5 Promoter in Advanced Colorectal Cancer Patients Treated With Oxaliplatin-Based Chemotherapy

The promoter is the center for regulation of gene transcription due to containing numerous transcription factor binding sites. The aim of the study was to determine whether genetic variations at excision repair cross complementation group 5 (ERCC5) promoter could affect transcription factor binding and whether such single nucleotide polymorphism (SNP)-dependent binding could affect gene expression, drug response, and clinical outcome.A total of 170 patients who were cytologically or histologically confirmed with advanced colorectal cancer (CRC), at least 1 measurable lesion, and underwent oxaliplatin-based chemotherapy were studied. The polymerase chain reaction-ligation detection reaction (PCR-LDR) was used to analyze SNPs. The reporter gene assay system and electrophoretic mobility shift assays (EMSA) were performed to investigate the effect of SNPs on the ERCC5 promoter activity and DNA-binding activity, respectively. The mRNA and protein expression of ERCC5 in tumor tissues of colorectal cancer patients with different genotypes were detected by real-time PCR and western blot, respectively.Both -763A and -763G allele had nuclear protein-binding ability. +25A allele did not show any nuclear protein-binding ability, whereas +25G allele did. The relative luciferase activity of the -763A/+25G haplotype was significantly higher than other 3 haplotypes (P < 0.05). The expression level of ERCC5 mRNA and protein was significantly higher in tumor tissues with -763AA+25GG genotype combination than that with -763GG+25AA genotype combination (P < 0.05, respectively). Allelic variants (-763AA vs -763AG or -763GG, +25GG versus +25AG or +25AA) were significantly associated with shorter progression-free survival (PFS) and overall survival (OS) (P < 0.05, respectively). At multivariate analysis, patients with risk genotypes (-763AA or +25GG genotype) demonstrated a significantly increasing risk of progression (P = 0.01) or worse OS (P = 0.001).The ERCC5 promoter polymorphisms at -763 and +25 may be important functional variants and predictors of clinical outcome of CRC patients who received oxaliplatin chemotherapy.

Iranian-Islamic traditional medicine: An ancient comprehensive personalized medicine

Personalized medicine (PM) is a novel term used for a medical model in which all diagnostic, prognostic, and therapeutic aspects of a disease are individualized for a patient using specific molecular testing. In Iranian-Islamic traditional medicine (IITM) an ancient paradigm for PM has been described which has been introduced in this paper. We reviewed the ancient resources of IITM and many valid recent studies on personalized medicine and described an ancient feature of personalized medicine in comparison with new ones. According to IITM scholars, every person has an individual temperament which is concluded of four basic humors combination. The individual temper is influenced by internal and external factors such as age, gender, ethnicity, season, and environment. This variability leads to different physical and mental behaviors toward a particular condition; so if we could identify the patient's temper, we would predict his/her health-related behaviors rather than predisposition and prognosis to different diseases, and select the best treatment. This holistic viewpoint of IITM to the human health and disease justifies the variable phenotypes among similar illnesses; the fact around which more advanced high-tech researches are being developed to explore all specific molecular pathways. IITM offers an ancient comprehensive PM (APM) which is more available and inexpensive compared to the modern PM (MPM). Moreover, APM focuses more on fitness than illness in comparison to MPM. It seems more attention to APM introduced by IITM could help us to promote health community. Design studies using high-tech MPM techniques would likely lead to clarification of most molecular aspects of APM.

Genotyping concordance in DNA extracted from formalin-fixed paraffin embedded (FFPE) breast tumor and whole blood for pharmacogenetic analyses

BACKGROUND

Cancer pharmacogenetic studies use archival tumor samples as a DNA source when germline DNA is unavailable. Genotyping DNA from formalin-fixed paraffin embedded tumors (FFPE-T) may be inaccurate due to FFPE storage, genetic aberrations, and/or insufficient DNA extraction. Our objective was to assess the extent and source of genotyping inaccuracy from FFPE-T DNA and demonstrate analytical validity of FFPE-T genotyping of candidate single nucleotide polymorphisms (SNPs) for pharmacogenetic analyses.

METHODS

Cancer pharmacogenetics SNPs were genotyped by Sequenom MassARRAYs in DNA harvested from matched FFPE-T, FFPE lymph node (FFPE-LN), and whole blood leukocyte samples obtained from breast cancer patients. No- and discordant-call rates were calculated for each tissue type and SNP. Analytical validity was defined as any SNP with <5% discordance between FFPE-T and blood and <10% discordance plus no-calls.

RESULTS

Matched samples from 114 patients were genotyped for 247 SNPs. No-call rate in FFPE-T was greater than FFPE-LN and blood (4.3% vs. 3.0% vs. 0.5%, p < 0.001). Discordant-call rate between FFPE-T and blood was very low, but greater than that between FFPE-LN and blood (1.1% vs. 0.3%, p < 0.001). Samples with heterozygous genotypes were more likely to be no- or discordantly-called in either tissue (p < 0.001). Analytical validity of FFPE-T genotyping was demonstrated for 218 (88%) SNPs.

CONCLUSIONS

No- and discordant-call rates were below concerning thresholds, confirming that most SNPs can be accurately genotyped from FFPE-T on our Sequenom platform. FFPE-T is a viable DNA source for prospective-retrospective pharmacogenetic analyses of clinical trial cohorts.