Pharmacogenomic Profiling of ADME Gene Variants: Current Challenges and Validation Perspectives

Development and validation of a prognostic signature of breast cancer based on drug absorption, distribution, metabolism and excretion (ADME)-related genes

The individual variation of carcinogenesis and drug response is influenced by the absorption, distribution, metabolism, and excretion (ADME) of drugs. The utilization of signatures derived from ADME-related genes holds potential for predicting prognosis and treatment response across diverse cancer types. Further investigation is required to completely understand the role of ADME-associated genes in breast cancer. A signature was constructed through the application of a least absolute shrinkage and selection operator regression model, employing prognostic differentially expressed genes found in both cancer tissue and normal tissue. To assess the robustness of the signature, verification analyses were carried out. RT-qPCR was utilized for the validation of gene expression related to risk. Subsequently, a nomogram was developed to enhance the clinical utility of our prognostic tool. The ADME signature, comprising four genes, was established and exhibited a robust association with the prognoses of individuals diagnosed with breast cancer. The nomogram was created by fusing the clinicopathological characteristics with the ADME signature. The ADME signature demonstrated remarkable superiority when compared to the performance of the other individual predictors. Additionally, the analysis of the immune microenvironment revealed that the ImmuneScores of the low-risk group were elevated. The variation in both the infiltration of immune cells and the expression of immune-related genes in the tissues differed among the two groups. For patients with breast cancer, the utilization of ADME signatures as biomarkers presents a significant reference point for prognosis and individualized treatment strategies.

Pharmacogenomics: A Genetic Approach to Drug Development and Therapy

The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body's reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

Characterization of CYP2C19 pharmacogenetic variation in African populations and comparison with other global populations

Background: CYP2C19 is important in the metabolism of clopidogrel and several antidepressants. This study aimed to characterize the distribution of CYP2C19 star alleles (haplotypes) across diverse African populations compared with global populations. Methods: CYP2C19 star alleles and diplotypes were called from high coverage genomes using the StellarPGx pipeline. Results: CYP2C19*1 (51%), *2 (17%) and *17 (22%) were the most common star alleles across African populations in this study. It was observed that 3% of African participants had potentially novel CYP2C19 haplotypes. Conclusion: This study supports the necessity for CYP2C19 pharmacogenetic testing in African and global clinical settings, as well as the importance of comprehensive star allele characterization in the African context.

First-line systemic treatment for hepatocellular carcinoma: A systematic review and network meta-analysis

The rapid development of novel therapeutic options for advanced hepatocellular carcinoma (aHCC) has generated some uncertainty about the rational choice of the systemic upfront treatment. So far, a variety of therapeutic strategies have been investigated, including the combination of immunecheckpoint inhibitors and anti-VEGF. To identify the treatment that should be preferred as front-line approach, we compared the efficacy and toxicity of a variety of therapeutic strategies. With this aim, we performed a systematic review and a meta-analysis of randomized clinical trials. OS, PFS, ORR and tolerability outcomes were considered, and for each outcome the treatment ranking was evaluated by the surface under the cumulative rankings (SUCRAs). Combination of Camrelizumab + Rivoceranib scored the best in OS, followed by Sintilimab + Bevacizumab, whereas Lenvatinib + Pembrolizumab showed higher probability to be the best treatment in PFS and Sintilimab + Bevacizumab performed best in ORR. Finally, Durvalumab is the most tolerated treatment.

Hit identification of novel small molecules interfering with MALAT1 triplex by a structure-based virtual screening

Nowadays, RNA is an attractive target for the design of new small molecules with different pharmacological activities. Among several RNA molecules, long noncoding RNAs (lncRNAs) are extensively reported to be involved in cancer pathogenesis. In particular, the overexpression of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays an important role in the development of multiple myeloma (MM). Starting from the crystallographic structure of the triple-helical stability element at the 3'-end of MALAT1, we performed a structure-based virtual screening of a large commercial database, previously filtered according to the drug-like properties. After a thermodynamic analysis, we selected five compounds for the in vitro assays. Compound M5, characterized by a diazaindene scaffold, emerged as the most promising molecule enabling the destabilization of the MALAT1 triplex structure and antiproliferative activity on in vitro models of MM. M5 is proposed as a lead compound to be further optimized for improving its affinity toward MALAT1.

Pembrolizumab plus lenvatinib in advanced endometrial cancer: case report and systematic review of lung toxicity

Background

The optimal strategy for the treatment of recurrent and/or advanced endometrial cancer is still undefined. Recently, despite the lack of any predictive biomarker, the combination of pembrolizumab with lenvatinib has improved survival outcomes. We here report the long-term management of lung toxicity in a patient with endometrial cancer, and we critically review the current therapeutic options for this disease.

Results

A patient with heavily pretreated endometrial cancer took pembrolizumab plus lenvatinib for 1 year, achieving a persistent partial response with a time to treatment failure of 18 months, despite relevant lung toxicity that did not affect the remarkable overall clinical benefit. A systematic review of this combination underlines the efficacy outcome despite toxicity. Interestingly, the literature review on lung toxicity suggested the role of anti-angiogenetic agents in the pathogenesis of lung cavitation, probably related to direct treatment activity, and disclosed a potential radiological sign predictive of the activity of anti-angiogenetic agents.

Conclusion

We underline the efficacy of pembrolizumab plus lenvatinib in the current treatment landscape of endometrial cancer, underscoring the relevance of a correct management of toxicity.

A Pronectin™ AXL-targeted first-in-class bispecific T cell engager (pAXLxCD3ε) for ovarian cancer

BACKGROUND

Pronectins™ are a new class of fibronectin-3-domain 14th-derived (14Fn3) antibody mimics that can be engineered as bispecific T cell engager (BTCE) to redirect immune effector cells against cancer. We describe here the in vitro and in vivo activity of a Pronectin™ AXL-targeted first-in-class bispecific T cell engager (pAXLxCD3ε) against Epithelial Ovarian Cancer (EOC).

METHODS

pAXLxCD3ε T-cell mediated cytotoxicity was evaluated by flow cytometry and bioluminescence. pAXLxCD3ε mediated T-cell infiltration, activation and proliferation were assessed by immunofluorescence microscopy and by flow cytometry. Activity of pAXLxCD3ε was also investigated in combination with poly-ADP ribose polymerase inhibitors (PARPi). In vivo antitumor activity of pAXLxCD3ε was evaluated in immunocompromised (NSG) mice bearing intraperitoneal or subcutaneous EOC xenografts and immunologically reconstituted with human peripheral blood mononuclear cells (PBMC).

RESULTS

pAXLxCD3ε induced dose-dependent cytotoxicity by activation of T lymphocytes against EOC cells, regardless of their histologic origin. The addition of PARPi to cell cultures enhanced pAXLxCD3ε cytotoxicity. Importantly, in vivo, pAXLxCD3ε was highly effective against EOC xenografts in two different NSG mouse models, by inhibiting the growth of tumor cells in ascites and subcutaneous xenografts. This effect translated into a significantly prolonged survival of treated animals.

CONCLUSION

pAXLxCD3ε is an active therapeutics against EOC cells providing a rational for its development as a novel agent in this still incurable disease. The preclinical validation of a first-in-class agent opens the way to the development of a new 14Fn3-based scaffold platform for the generation of innovative immune therapeutics against cancer.

The First-In-Class Anti-AXL×CD3ε Pronectin™-Based Bispecific T-Cell Engager Is Active in Preclinical Models of Human Soft Tissue and Bone Sarcomas

Sarcomas are heterogeneous malignancies with limited therapeutic options and a poor prognosis. We developed an innovative immunotherapeutic agent, a first-in-class Pronectin™-based Bispecific T-Cell Engager (pAXL×CD3ε), for the targeting of AXL, a TAM family tyrosine kinase receptor highly expressed in sarcomas. AXL expression was first analyzed by flow cytometry, qRT-PCR, and Western blot on a panel of sarcoma cell lines. The T-cell-mediated pAXL×CD3ε cytotoxicity against sarcoma cells was investigated by flow cytometry, luminescence assay, and fluorescent microscopy imaging. The activation and degranulation of T cells induced by pAXL×CD3ε were evaluated by flow cytometry. The antitumor activity induced by pAXL×CD3ε in combination with trabectedin was also investigated. In vivo activity studies of pAXL×CD3ε were performed in immunocompromised mice (NSG), engrafted with human sarcoma cells and reconstituted with human peripheral blood mononuclear cells from healthy donors. Most sarcoma cells showed high expression of AXL. pAXL×CD3ε triggered T-lymphocyte activation and induced dose-dependent T-cell-mediated cytotoxicity. The combination of pAXL×CD3ε with trabectedin increased cytotoxicity. pAXL×CD3ε inhibited the in vivo growth of human sarcoma xenografts, increasing the survival of treated mice. Our data demonstrate the antitumor efficacy of pAXL×CD3ε against sarcoma cells, providing a translational framework for the clinical development of pAXL×CD3ε in the treatment of human sarcomas, aggressive and still-incurable malignancies.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Development and Validation of an ADME-Related Gene Signature for Survival, Treatment Outcome and Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma

ADME genes are a set of genes which are involved in drug absorption, distribution, metabolism, and excretion (ADME). However, prognostic value and function of ADME genes in head and neck squamous cell carcinoma (HNSCC) remain largely unclear. In this study, we established an ADME-related prognostic model through the least absolute shrinkage and selection operator (LASSO) analysis in the Cancer Genome Atla (TCGA) training cohort and its robustness was validated by TCGA internal validation cohort and a Gene Expression Omnibus (GEO) external cohort. The 14-gene signature stratified patients into high- or low-risk groups. Patients with high-risk scores exhibited significantly poorer overall survival (OS) and disease-free survival (DFS) than those with low-risk scores. Receiver operating characteristic (ROC) curve analysis was used to confirm the signature’s predictive efficacy for OS and DFS. Furthermore, gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses showed that immune-related functions and pathways were enriched, such as lymphocyte activation, leukocyte cell-cell adhesion and T-helper cell differentiation. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and other analyses revealed that immune cell (especially B cell and T cell) infiltration levels were significantly higher in the low-risk group. Moreover, patients with low-risk scores were significantly associated with immunotherapy and chemotherapy treatment benefit. In conclusion, we constructed a novel ADME-related prognostic and therapeutic biomarker associated with immune cell infiltration of HNSCC patients.

Microarray Data Analysis Protocol

Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.

Autoimmune colitis and neutropenia in adjuvant anti-PD-1 therapy for malignant melanoma: efficacy of Vedolizumab, a case report

Immune checkpoint inhibitors (ICIs) represent an important advance in the adjuvant treatment of patients with high-risk melanoma. Although the safety profile of anti-programmed cell death protein-1 (PD-1) is fairly acceptable, different immune-related adverse events (irAEs) are described. Herein we report for the first time a notably multidisciplinary combined approach on a malignant melanoma (MM) patient treated with anti-PD-1 antibody in adjuvant setting. In this novel approach, corticosteroid-refractory immune-mediated colitis (IMC) was effectively treated with Vedolizumab, a selective blockade of the α4β7 integrin and corticosteroids were successfully administered for autoimmune neutropenia. Notably, our patient also express HLA-B*35, a potential biomarker for predicting a genetic basis of autoimmune susceptibility. Our experience offers a possible future perspective about the use of Vedolizumab together with immunotherapy in a strategic early approach for high-risk patients genotyped for HLA.

Tools in Pharmacogenomics Biomarker Identification for Cancer Patients

The understanding of the biological differences which underlie the inter-individual variability in drug response improved the efficacy of cancer therapy in the era of precision medicine. In fact molecularly targeted drugs and immunotherapy represent a revolution in cancer treatment. The identification of genetic predictive and/or prognostic biomarkers linked to drug pharmacokinetics (PK) and pharmacodynamics (PD) is allowed by the development of high-throughput omics tools for detecting and understanding biological differences among individuals, in order to improve drug efficacy and minimize risk of toxicity. Personalized medicine in cancer treatment reduces costs of the healthcare system. Unfortunately, pharmacogenomics biomarkers discovery is influenced by complexity, need of high-quality evidence, and a validation process for regulatory purposes. This chapter is focused on the critic analysis of presently available pharmacogenomics tools for discovering or testing genetic polymorphic variants in drug metabolizing enzyme to be introduced in clinical practice for the prospective stratification of cancer patients.

Integration of DNA Microarray with Clinical and Genomic Data

DNA microarrays have been widely employed to understand cancer development. This technology is able to measure expression levels of a large numbers of genes or to genotype multiple regions of a genome in a massively parallel experiment. In addition, the detection of methylation patterns and gene copy number variations are also performed. Clinicians began to apply these findings in personalized medicine for the selection of cancer therapy according to the individual's cancer genomic profile. Because cancer is a complex disease it is of great value to integrate microarray data with genomic and clinical data. Here, we presented an overview of DNA microarray technology and discuss about benefits and challenging of microarray data integration.

Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.In particular, it leads towards a comparative analysis of RNA-Seq data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM ) Survival Analysis and an enrichment analysis for a Differential Gene Expression (DGE) gene set.Furthermore, it deals with MMRF-RG data for analyzing the correlation between canonical variants and treatment outcome as well as treatment class. In order to show the potential of the workflow, we present two case studies. The former deals with data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class.

Analytical validation of a laboratory-development multigene pharmacogenetic assay

OBJECTIVE

The implementation of pharmacogenetics (PGx) in clinical practice is an essential tool for personalized medicine. However, clinical laboratories must validate their procedures before being used to perform PGx studies in patients, in order to confirm that they are adequate for the intended purposes.

METHODS

We designed a validation process for our in-house pharmacogenetic PCR-based method assay.

RESULTS

The concordance to reference, repeatability and reproducibility was 100%. Sensitivity and specificity were 100% for the detection of variant diplotypes in CYP2C9, CYP3A5, TPMT, DPYD and UGT1A1 genes. The sensitivity was lower in the detection of CYP2C19 variants due to a limitation in the design that prevents the detection of CYP2C19 *2/*10 diplotype.

CONCLUSIONS

The success of implementing clinical pharmacogenetic testing into routine clinical practice is dependent on the precision of genotyping. Limitations must be bearing in mind to guarantee the quality of PGx assays in clinical laboratory practice. We provided objective evidence that the necessary requirements in our laboratory-development assay were fulfilled.

Individualized Drugs' Selection by Evaluation of Drug Properties, Pharmacogenomics and Clinical Parameters: Performance of a Bioinformatic Tool Compared to a Clinically Established Counselling Process

Purpose

Inefficacy and safety concerns are main medications’ problems, especially in the case of poly-therapies, when drug–drug interactions may alter the expected drug disposition. Ongoing efforts are aimed to establish drug selection processes aimed to preemptive evaluation of a plethora of factors affecting patient’s specific drug response, including pharmacogenomic markers, in order to minimize prescription of improper medications. In previous years, we established at the University Hospital Sant’Andrea of Rome, Italy, a Precision Medicine Service based on a multi-disciplinary experts’ team. The team is in charge to produce a drug therapy counselling report, including pharmacogenomic, pharmacokinetic and pharmacodynamic considerations. In this study, we aimed to evaluate the performance of this established “manual” process of therapy selection with a novel bioinformatic tool, the Drug-PIN system.

Patients and Methods

A total of 200 patients diagnosed with Major Depressive Disorders or a depressive episode in Bipolar Disorder, with at least three previous failed treatments, who underwent pharmacogenomic profiling and therapy counselling in the Sant’Andrea Hospital from 2017 to 2020. The baseline poly-therapy of these patients was re-evaluated and optimized by Drug-PIN. Results of the Drug-PIN poly-therapy evaluation/optimization were compared with the results of the original poly-therapy evaluation/optimization by therapy counselling. To compare the results between the two processes, the risk associated with each poly-therapy was classified as low, moderate, or high.

Results

The number of baseline poly-therapies classified in low-, moderate- or high-risk did not change significantly between manual system or Drug-PIN system. As the counselling process, also the Drug-PIN system produces a significant decrease in the predicted treatment-associated risk.

Conclusion

Drug-PIN substantially replicates the output of the counselling process, allowing a substantial reduction in the time needed for therapy evaluation. Availability of an effective bioinformatic tool for proper drug selection is expected to exponentially increase the actuation of targeted therapy strategies.

Pharmacogenomics Biomarker Discovery and Validation for Translation in Clinical Practice

Interindividual variability in drug efficacy and toxicity is a major challenge in clinical practice. Variations in drug pharmacokinetics (PKs) and pharmacodynamics (PDs) can be, in part, explained by polymorphic variants in genes encoding drug metabolizing enzymes and transporters (absorption, distribution, metabolism, and excretion) or in genes encoding drug receptors. Pharmacogenomics (PGx) has allowed the identification of predictive biomarkers of drug PKs and PDs and the current knowledge of genome-disease and genome-drug interactions offers the opportunity to optimize tailored drug therapy. High-throughput PGx genotyping, from targeted to more comprehensive strategies, allows the identification of PK/PD genotypes to be developed as clinical predictive biomarkers. However, a biomarker needs a robust process of validation followed by clinical-grade assay development and must comply to stringent regulatory guidelines. We here discuss the methodological challenges and the emerging technological tools in PGx biomarker discovery and validation, at the crossroad among molecular genetics, bioinformatics, and clinical medicine.

Symptom Experience, Management, and Outcomes According to Race and Social Determinants Including Genomics, Epigenomics, and Metabolomics (SEMOARS + GEM): an Explanatory Model for Breast Cancer Treatment Disparity

Even after controlling for stage, comorbidity, age, and insurance status, black women with breast cancer (BC) in the USA have the lowest 5-year survival as compared with all other races for stage-matched disease. One potential cause of this survival difference is the disparity in cancer treatment, evident in many population clinical trials. Specifically, during BC chemotherapy, black women receive less relative dose intensity with more dose reductions and early chemotherapy cessation compared with white women. Symptom incidence, cancer-related distress, and ineffective communication, including the disparity in patient-centeredness of care surrounding patient symptom reporting and clinician assessment, are important factors contributing to racial disparity in dose reduction and early therapy termination. We present an evidence-based overview and an explanatory model for racial disparity in the symptom experience during BC chemotherapy that may lead to a reduction in dose intensity and a subsequent disparity in outcomes. This explanatory model, the Symptom Experience, Management, Outcomes and Adherence according to Race and Social determinants + Genomics Epigenomics and Metabolomics (SEMOARS + GEM), considers essential factors such as social determinants of health, clinician communication, symptoms and symptom management, genomics, epigenomics, and pharmacologic metabolism as contributory factors.

DMETTM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine

The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMET^TM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMET^TM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMET^TM Platform).