CYP2D6 phenotype, tamoxifen, and risk of contralateral breast cancer in the WECARE Study

Developing an innovative 3D printing platform for production of personalised medicines in a hospital for the OPERA clinical trial

Breast cancer is the most frequently diagnosed cancer in women worldwide, and non-adherence to adjuvant hormonotherapy can negatively impact cancer recurrence and relapse. Non-adherence is associated with side effects of hormonotherapy. Pharmacological strategies to mitigate the side effects include coadministration of antidepressants, however patients remain non-adherent. The aim of this work was to develop medicines containing both hormonotherapy, tamoxifen (20 mg), along with anti-depressants, either venlafaxine (37.5 or 75 mg) or duloxetine (30 or 60 mg), to assess the acceptability and efficacy of this personalised approach for mitigating tamoxifen side effects in a clinical trial. A major criterion for the developed medicines was the production rate, specified at minimum 200 dosage units per hour to produce more than 40,000 units required for the clinical trial. A novel capsule filling approach enabled by the pharmaceutical 3D printer M3DIMAKER 2 was developed for this purpose. Firstly, semi-solid extrusion 3D printing enabled the filling of tamoxifen pharma-ink prepared according to French compounding regulation, followed by filling of commercial venlafaxine or duloxetine pellets enabled by the development of an innovative pellet dispensing printhead. The medicines were successfully developed and produced in the clinical pharmacy department of the cancer hospital Gustave Roussy, located in Paris, France. The developed medicines satisfied quality and production rate requirements and were stable for storage up to one year to cover the duration of the trial. This work demonstrates the feasibility of developing and producing combined tamoxifen medicines in a hospital setting through a pharmaceutical 3D printer to enable a clinical trial with a high medicines production rate requirement.

Hallmarks of cancer resistance

This review explores the hallmarks of cancer resistance, including drug efflux mediated by ATP-binding cassette (ABC) transporters, metabolic reprogramming characterized by the Warburg effect, and the dynamic interplay between cancer cells and mitochondria. The role of cancer stem cells (CSCs) in treatment resistance and the regulatory influence of non-coding RNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are studied. The chapter emphasizes future directions, encompassing advancements in immunotherapy, strategies to counter adaptive resistance, integration of artificial intelligence for predictive modeling, and the identification of biomarkers for personalized treatment. The comprehensive exploration of these hallmarks provides a foundation for innovative therapeutic approaches, aiming to navigate the complex landscape of cancer resistance and enhance patient outcomes.

A genome-wide association study of contralateral breast cancer in the Women's Environmental Cancer and Radiation Epidemiology Study

BACKGROUND

Contralateral breast cancer (CBC) is the most common second primary cancer diagnosed in breast cancer survivors, yet the understanding of the genetic susceptibility of CBC, particularly with respect to common variants, remains incomplete. This study aimed to investigate the genetic basis of CBC to better understand this malignancy.

FINDINGS

We performed a genome-wide association analysis in the Women's Environmental Cancer and Radiation Epidemiology (WECARE) Study of women with first breast cancer diagnosed at age < 55 years including 1161 with CBC who served as cases and 1668 with unilateral breast cancer (UBC) who served as controls. We observed two loci (rs59657211, 9q32, SLC31A2/FAM225A and rs3815096, 6p22.1, TRIM31) with suggestive genome-wide significant associations (P < 1 × 10-6). We also found an increased risk of CBC associated with a breast cancer-specific polygenic risk score (PRS) comprised of 239 known breast cancer susceptibility single nucleotide polymorphisms (SNPs) (rate ratio per 1-SD change: 1.25; 95% confidence interval 1.14-1.36, P < 0.0001). The protective effect of chemotherapy on CBC risk was statistically significant only among patients with an elevated PRS (Pheterogeneity = 0.04). The AUC that included the PRS and known breast cancer risk factors was significantly elevated.

CONCLUSIONS

The present GWAS identified two previously unreported loci with suggestive genome-wide significance. We also confirm that an elevated risk of CBC is associated with a comprehensive breast cancer susceptibility PRS that is independent of known breast cancer risk factors. These findings advance our understanding of genetic risk factors involved in CBC etiology.

CYP2D6 genotyping and the clinical impact on outcomes in breast cancer tamoxifen-treated patients

Aims: To report the distribution of allele frequencies of CYP2D6 gene and to evaluate their influence on the clinical outcomes of a group of breast cancer patients receiving adjuvant tamoxifen treatment from Uruguay. Patients & methods: 199 samples were genotyped through real-time polymerase chain reaction assays. Metabolization profiles were inferred from the genotypes. Correlations were evaluated using Pearson's χ2 test. Results: Phenotype frequencies were 0.65 normal (NM), 0.30 intermediate (IM) and 0.05 poor metabolizers (PM). Similar clinical outcomes between NM and (PM + IM) patient groups (odds ratio = 1.011, 95% CI = 0.2703-3.7826; p = 0.987) were found. Conclusion: CYP2D6 allele frequencies were analyzed for the first time in a cohort from Uruguay. Results did not support any impact of CYP2D6 gene polymorphisms on clinical outcomes.

EA, Udupa KS, Gota V, Mallayasamy S. Factors Influencing Pharmacokinetics of Tamoxifen in Breast Cancer Patients: A Systematic Review of Population Pharmacokinetic Models

BACKGROUND

Tamoxifen is useful in managing breast cancer and it is reported to have significant variability in its pharmacokinetics. This review aimed to summarize reported population pharmacokinetics studies of tamoxifen and to identify the factors affecting the pharmacokinetics of tamoxifen in adult breast cancer patients.

METHOD

A systematic search was undertaken in Scopus, Web of Science, and PubMed for papers published in the English language from inception to 20 August 2022. Studies were included in the review if the population pharmacokinetic modeling was based on non-linear mixed-effects modeling with a parametric approach for tamoxifen in breast cancer patients.

RESULTS

After initial selection, 671 records were taken for screening. A total of five studies were selected from Scopus, Web of Science, PubMed, and by manual searching. The majority of the studies were two-compartment models with first-order absorption and elimination to describe tamoxifen and its metabolites' disposition. The CYP2D6 phenotype and CYP3A4 genotype were the main covariates that affected the metabolism of tamoxifen and its metabolites. Other factors influencing the drug's pharmacokinetics included age, co-medication, BMI, medication adherence, CYP2B6, and CYP2C19 genotype.

CONCLUSION

The disposition of tamoxifen and its metabolites varies primarily due to the CYP2D6 phenotype and CYP3A4 genotype. However, other factors, such as anthropometric characteristics and menopausal status, should also be addressed when accounting for this variability. All these studies should be externally evaluated to assess their applicability in different populations and to use model-informed dosing in the clinical setting.

Loss of ARID1A expression is associated with systemic inflammation markers and has important prognostic significance in gastric cancer

BACKGROUND

The tumor suppressor gene AT-rich interactive domain 1A (ARID1A) and systemic inflammatory response (SIR) have been reported to be related to the sensitivity to immunotherapy. This study intended to explore the relationship between ARID1A expression and SIR, and to further elucidate the prognostic value of ARID1A expression in gastric cancer (GC).

METHODS

The mRNA and protein expression of ARID1A were detected in 272 formalin-fixed paraffin-embedded (FFPE) tumor tissues. The data of nine systemic inflammation markers were collected 1 week before gastrectomy. Univariate and multivariate COX analysis were used to screen out independent predictors of GC.

RESULTS

Negative expression of ARID1A protein was related to GC with deficient mismatch repair (dMMR) (p = 0.033), positive programmed cell death-ligand 1 (PD-L1) (p = 0.005) and lower albumin level (p = 0.0064). Low expression of ARID1A mRNA was common in GC with abnormal E-cadherin (p = 0.020) and a higher platelet/lymphocyte ratio (PLR) (p = 0.0391). Multivariate COX analysis showed that the expression of ARID1A protein (p = 0.023), age (p = 0.004), T stage (p = 0.009) and N stage (p = 0.009) were independent predictors of GC. The nomogram established by independent predictors can accurately evaluate the survival risk of patients with GC.

CONCLUSIONS

The loss of ARID1A protein expression was associated with the dMMR subtype and high expression of PD-L1 in GC. Negative ARID1A protein and low expression of mRNA were associated with aberrant systemic inflammatory markers. The expression of ARID1A protein had important prognostic significance in GC.

SWAAT Bioinformatics Workflow for Protein Structure-Based Annotation of ADME Gene Variants

Recent genomic studies have revealed the critical impact of genetic diversity within small population groups in determining the way individuals respond to drugs. One of the biggest challenges is to accurately predict the effect of single nucleotide variants and to get the relevant information that allows for a better functional interpretation of genetic data. Different conformational scenarios upon the changing in amino acid sequences of pharmacologically important proteins might impact their stability and plasticity, which in turn might alter the interaction with the drug. Current sequence-based annotation methods have limited power to access this type of information. Motivated by these calls, we have developed the Structural Workflow for Annotating ADME Targets (SWAAT) that allows for the prediction of the variant effect based on structural properties. SWAAT annotates a panel of 36 ADME genes including 22 out of the 23 clinically important members identified by the PharmVar consortium. The workflow consists of a set of Python codes of which the execution is managed within Nextflow to annotate coding variants based on 37 criteria. SWAAT also includes an auxiliary workflow allowing a versatile use for genes other than ADME members. Our tool also includes a machine learning random forest binary classifier that showed an accuracy of 73%. Moreover, SWAAT outperformed six commonly used sequence-based variant prediction tools (PROVEAN, SIFT, PolyPhen-2, CADD, MetaSVM, and FATHMM) in terms of sensitivity and has comparable specificity. SWAAT is available as an open-source tool.

Clinical CYP2D6 Genotyping to Personalize Adjuvant Tamoxifen Treatment in ER-Positive Breast Cancer Patients: Current Status of a Controversy

Simple Summary

Tamoxifen is an important adjuvant endocrine therapy in estrogen receptor (ER)-positive breast cancer patients. It is mainly catalyzed by the enzyme CYP2D6 into the most active metabolite endoxifen. Genetic variation in the CYP2D6 gene influences endoxifen formation and thereby potentially therapy outcome. However, the association between CYP2D6 genotype and clinical outcome on tamoxifen is still under debate, as contradictory outcomes have been published. This review describes the latest insights in both CYP2D6 genotype and endoxifen concentrations, as well CYP2D6 genotype and clinical outcome, from 2018 to 2020.

Abstract

Tamoxifen is a major option for adjuvant endocrine treatment in estrogen receptor (ER) positive breast cancer patients. The conversion of the prodrug tamoxifen into the most active metabolite endoxifen is mainly catalyzed by the enzyme cytochrome P450 2D6 (CYP2D6). Genetic variation in the CYP2D6 gene leads to altered enzyme activity, which influences endoxifen formation and thereby potentially therapy outcome. The association between genetically compromised CYP2D6 activity and low endoxifen plasma concentrations is generally accepted, and it was shown that tamoxifen dose increments in compromised patients resulted in higher endoxifen concentrations. However, the correlation between CYP2D6 genotype and clinical outcome is still under debate. This has led to genotype-based tamoxifen dosing recommendations by the Clinical Pharmacogenetic Implementation Consortium (CPIC) in 2018, whereas in 2019, the European Society of Medical Oncology (ESMO) discouraged the use of CYP2D6 genotyping in clinical practice for tamoxifen therapy. This paper describes the latest developments on CYP2D6 genotyping in relation to endoxifen plasma concentrations and tamoxifen-related clinical outcome. Therefore, we focused on Pharmacogenetic publications from 2018 (CPIC publication) to 2021 in order to shed a light on the current status of this debate.

Recent advances in the personalized treatment of estrogen receptor-positive breast cancer with tamoxifen: a focus on pharmacogenomics

Introduction: Tamoxifen is still an important drug in hormone-dependent breast cancer therapy. Personalization of its clinical use beyond hormone receptor positivity could improve the substantial variability of the treatment response.Areas covered: The overview of the current evidence for the treatment personalization using therapeutic drug monitoring, or using genetic biomarkers including CYP2D6 is provided. Although many studies focused on the PK aspects or the impact of CYP2D6 variability the translation into clinical routine is not clearly defined due to the inconsistent clinical outcome data.Expert opinion: We believe that at least the main candidate factors, i.e. CYP2D6 polymorphism, CYP2D6 inhibition, endoxifen serum levels may become important predictors of clinical relevance for tamoxifen treatment personalization in the future. To achieve this aim, however, further research should take into consideration more precise characterization of the disease, epigenetic factors and also utilize an appropriately powered multifactorial approach instead of a single gene evaluating studies.

The Role of CYP2D6 Polymorphisms in Determining Response to Tamoxifen in Metastatic Breast Cancer Patients: Review and Egyptian Experience

Background:

Metastatic breast cancer (MBC) represents a major health problem in Egypt and worldwide. Prognostic and predictive factors for patients with MBC are highly required for better management and improved survival. The aim of this study was to assess the prognostic and predictive value(s) of CYP2D6 polymorphisms in Tamoxifen responders and non-responders.

Methods:

A cohort of 157 hormone receptor positive, locally recurrent inoperable and/or metastatic (MBC) Egyptian female patients was assessed for CYP2D6 polymorphisms. Data were correlated to relevant clinic-pathological features of the patients, response to tamoxifen, and survival rates.

Results:

CYP2D6 polymorphisms were detected in 44/157 cases (28%), 30 of them (68.2%) were refractory and 14 (31.8%) were responders (P=0.027). The CYP2D6 *3,*4 variants were significantly prevalent in the refractory group 26/30 (86.6%), while the *10/*10 and *10/*3 variants were more common in the responders 12/14 (85.71%, P=0.027). CYP2D6 polymorphism associated significantly with Her-2 amplification (P=0.001) as well as reduced overall survival rates in both refractory and responder patients (P< 0.001).

Conclusion:

CYP2D6 polymorphisms can significantly predict response to Tamoxifen treatment, and also associates with poor overall survival rates in MBC patients.

Pharmacogenomics meets precision cardio-oncology: is there synergistic potential?

An individual's inherited genetic makeup and acquired genomic variants may account for a significant portion of observable variability in therapy efficacy and toxicity. Pharmacogenomics (PGx) is the concept that treatments can be modified to account for these differences to increase chances of therapeutic efficacy while minimizing risk of adverse effects. This is particularly applicable to oncology in which treatment may be multimodal. Each tumor type has a unique genomic signature that lends to inclusion of targeted therapy but may be associated with cumulative toxicity, such as cardiotoxicity, and can impact quality of life. A greater understanding of therapeutic agents impacted by PGx and subsequent implementation has the potential to improve outcomes and reduce risk of drug-induced adverse effects.

Predicting steady-state endoxifen plasma concentrations in breast cancer patients by CYP2D6 genotyping or phenotyping. Which approach is more reliable?

In previous studies, steady-state Z-endoxifen plasma concentrations (ENDOss) correlated with relapse-free survival in women on tamoxifen (TAM) treatment for breast cancer. ENDOss also correlated significantly with CYP2D6 genotype (activity score) and CYP2D6 phenotype (dextromethorphan test). Our aim was to ascertain which method for assessing CYP2D6 activity is more reliable in predicting ENDOss. The study concerned 203 Caucasian women on tamoxifen-adjuvant therapy (20 mg q.d.). Before starting treatment, CYP2D6 was genotyped (and activity scores computed), and the urinary log(dextromethorphan/dextrorphan) ratio [log(DM/DX)] was calculated after 15 mg of oral dextromethorphan. Plasma concentrations of TAM, N-desmethyl-tamoxifen (ND-TAM), Z-4OH-tamoxifen (4OH-TAM) and ENDO were assayed 1, 4, and 8 months after first administering TAM. Multivariable regression analysis was used to identify the clinical and laboratory variables predicting log-transformed ENDOss (log-ENDOss). Genotype-derived CYP2D6 phenotypes (PM, IM, NM, EM) and log(DM/DX) correlated independently with log-ENDOss. Genotype-phenotype concordance was almost complete only for poor metabolizers, whereas it emerged that 34% of intermediate, normal, and ultrarapid metabolizers were classified differently based on log(DM/DX). Multivariable regression analysis selected log(DM/DX) as the best predictor, with patients' age, weak inhibitor use, and CYP2D6 phenotype decreasingly important: log-ENDOss = 0.162 - log(DM/DX) × 0.170 + age × 0.0063 - weak inhibitor use × 0.250 + IM × 0.105 + (NM + UM) × 0.210; (R2  = 0.51). In conclusion, log(DM/DX) seems superior to genotype-derived CYP2D6 phenotype in predicting ENDOss.