Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet. 2013;14:23-34. [PMID: 23183705 DOI: 10.1038/nrg3352]

Precision oncology: current and future platforms for treatment selection

Genomic profiling of hundreds of cancer-associated genes is now a component of routine cancer care. DNA sequencing can identify mutations, mutational signatures, and structural alterations predictive of therapy response and assess for heritable cancer risk, but it has been less useful for identifying predictive biomarkers of sensitivity to cytotoxic chemotherapies, antibody drug conjugates, and immunotherapies. The clinical adoption of molecular profiling platforms such as RNA sequencing better suited to identifying those patients most likely to respond to immunotherapies and drug combinations will be critical to expanding the benefits of precision oncology. This review discusses the potential advantages of innovative molecular and functional profiling platforms designed to replace or complement targeted DNA sequencing and the major hurdles to their clinical adoption.

A Predictive Noninvasive Single-Nucleotide Variation-Based Biomarker Signature for Resectable Pancreatic Cancer: Protocol for a Prospective Validation Study

BACKGROUND

Single-nucleotide variations (SNVs; formerly SNPs) are inherited genetic variants that can be easily determined in routine clinical practice using a simple blood or saliva test. SNVs have potential to serve as noninvasive biomarkers for predicting cancer-specific patient outcomes after resection of pancreatic ductal adenocarcinoma (PDAC). Two recent analyses led to the identification and validation of three SNVs in the CD44 and CHI3L2 genes (rs187115, rs353630, and rs684559), which can be used as predictive biomarkers to help select patients most likely to benefit from pancreatic resection. These variants were associated with an over 2-fold increased risk for tumor-related death in three independent PDAC study cohorts from Europe and the United States, including The Cancer Genome Atlas cohorts (reaching a P value of 1×10-8). However, these analyses were limited by the inherent biases of a retrospective study design, such as selection and publication biases, thereby limiting the clinical use of these promising biomarkers in guiding PDAC therapy.

OBJECTIVE

To overcome the limitations of previous retrospectively designed studies and translate the findings into clinical practice, we aim to validate the association of the identified SNVs with survival in a controlled setting using a prospective cohort of patients with PDAC following pancreatic resection.

METHODS

All patients with PDAC who will undergo pancreatic resection at three participating hospitals in Switzerland and fulfill the inclusion criteria will be included in the study consecutively. The SNV genotypes will be determined using standard genotyping techniques from patient blood samples. For each genotyped locus, log-rank and Cox multivariate regression tests will be performed, accounting for the relevant covariates American Joint Committee on Cancer stage and resection status. Clinical follow-up data will be collected for at least 3 years. Sample size calculation resulted in a required sample of 150 patients to sufficiently power the analysis.

RESULTS

The follow-up data collection started in August 2019 and the estimated end of data collection will be in May 2027. The study is still recruiting participants and 142 patients have been recruited as of November 2023. The DNA extraction and genotyping of the SNVs will be performed after inclusion of the last patient. Since no SNV genotypes have been determined, no data analysis has been performed to date. The results are expected to be published in 2027.

CONCLUSIONS

This is the first prospective study of the CD44 and CHI3L2 SNV-based biomarker signature in PDAC. A prospective validation of this signature would enable its clinical use as a noninvasive predictive biomarker of survival after pancreatic resection that is readily available at the time of diagnosis and can assist in guiding PDAC therapy. The results of this study may help to individualize treatment decisions and potentially improve patient outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/54042.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Jelleine, a Family of Peptides Isolated from the Royal Jelly of the Honey Bees (Apis mellifera), as a Promising Prototype for New Medicines: A Narrative Review

The jelleine family is a group of four peptides (jelleines I-IV) originally isolated from the royal jelly of honey bee (Apis mellifera), but later detected in some honey samples. These oligopeptides are composed of 8-9 amino acid residues, positively charged (+2 to +3 at pH 7.2), including 38-50% of hydrophobic residues and a carboxamide C-terminus. Jelleines, generated by processing of the C-terminal region of major royal jelly proteins 1 (MRJP-1), play an important biological role in royal jelly conservation as well as in protecting bee larvae from potential pathogens. Therefore, these molecules present numerous benefits for human health, including therapeutic purposes as shown in preclinical studies. In this review, we aimed to evaluate the biological effects of jelleines in addition to characterising their toxicities and stabilities. Jelleines I-III have promising antimicrobial activity and low toxicity (LD50 > 1000 mg/Kg). However, jelleine-IV has not shown relevant biological potential. Jelleine-I, but not the other analogues, also has antiparasitic, healing, and pro-coagulant activities in addition to indirectly modulating tumor cell growth and controlling the inflammatory process. Although it is sensitive to hydrolysis by proteases, the addition of halogens increases the chemical stability of these molecules. Thus, these results suggest that jelleines, especially jelleine-I, are a potential target for the development of new, effective and safe therapeutic molecules for clinical use.

Cardio-oncology: Shared Genetic, Metabolic, and Pharmacologic Mechanism

PURPOSE OF REVIEW

The article aims to investigate the complex relationship between cancer and cardiovascular disease (CVD), with a focus on the effects of cancer treatment on cardiac health.

RECENT FINDINGS

Advances in cancer treatment have improved long-term survival rates, but CVD has emerged as a leading cause of morbidity and mortality in cancer patients. The interplay between cancer itself, treatment methods, homeostatic changes, and lifestyle modifications contributes to this comorbidity. Recent research in the field of cardio-oncology has revealed common genetic mutations, risk factors, and metabolic features associated with the co-occurrence of cancer and CVD. This article provides a comprehensive review of the latest research in cardio-oncology, including common genetic mutations, risk factors, and metabolic features, and explores the interactions between cancer treatment and CVD drugs, proposing novel approaches for the management of cancer and CVD.

Pharmacogenomics in the Management of Pulmonary Arterial Hypertension: Current Perspectives

Pulmonary arterial hypertension (PAH) is a rare disease with heterogeneous causes that can lead to right ventricular (RV) failure and death if left untreated. There are currently 10 medications representative of five unique pharmacologic classes that are approved for treatment. These have led to significant improvements in overall clinical outcome. However, substantial variability in dosing requirements and treatment response is evident, leading to suboptimal outcome for many patients. Furthermore, dosing is empiric and iterative and can lead to delays in meeting treatment goals and burdensome adverse effects. Pharmacogenomic (PGx) associations have been reported with certain PAH medications, such as treprostinil and bosentan, and can explain some of the variability in response. Relevant genes associated with treprostinil include CYP2C8, CYP2C9, CAMK2D, and PFAS. CYP2C8 and CYP2C9 are the genes encoding the major metabolizing liver enzymes for treprostinil, and reduced function variants (*2, *3) with CYP2C9 were associated with lower treatment persistence. Additionally, a higher CYP2C9 activity score was associated with a significantly less risk of treatment discontinuation. Other genes of interest that have been explored with treprostinil include CAMK2D, which is associated with right ventricular dysfunction and significantly higher dose requirements. Similarly, PFAS is associated with lower concentrations of cyclic adenosine monophosphate and significantly higher dose requirements. Genes of interest with the endothelin receptor antagonist (ERA) class include GNG2 and CYP2C9. A genetic variant in GNG2 (rs11157866) was linked to a significantly increased rate of clinical improvement with ERAs. The *2 variant with CYP2C9 (encoding for the major metabolizing enzyme for bosentan) was significantly associated with a higher risk for elevations in hepatic aminotransferases and liver injury. In summary, this article reviews the relevant pharmacogenes that have been associated to date with dosing and outcome among patients who received PAH medications.

Dose Optimization in Oncology Drug Development: The Emerging Role of Pharmacogenomics, Pharmacokinetics, and Pharmacodynamics

Drugs' safety and effectiveness are evaluated in randomized, dose-ranging trials in most therapeutic areas. However, this is only sometimes feasible in oncology, and dose-ranging studies are mainly limited to Phase 1 clinical trials. Moreover, although new treatment modalities (e.g., small molecule targeted therapies, biologics, and antibody-drug conjugates) present different characteristics compared to cytotoxic agents (e.g., target saturation limits, wider therapeutic index, fewer off-target side effects), in most cases, the design of Phase 1 studies and the dose selection is still based on the Maximum Tolerated Dose (MTD) approach used for the development of cytotoxic agents. Therefore, the dose was not optimized in some cases and was modified post-marketing (e.g., ceritinib, dasatinib, niraparib, ponatinib, cabazitaxel, and gemtuzumab-ozogamicin). The FDA recognized the drawbacks of this approach and, in 2021, launched Project Optimus, which provides the framework and guidance for dose optimization during the clinical development stages of anticancer agents. Since dose optimization is crucial in clinical development, especially of targeted therapies, it is necessary to identify the role of pharmacological tools such as pharmacogenomics, therapeutic drug monitoring, and pharmacodynamics, which could be integrated into all phases of drug development and support dose optimization, as well as the chances of positive clinical outcomes.

Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram

Objectives

To develop and validate a CT-based radiomics nomogram that can provide individualized pretreatment prediction of the response to platinum treatment in small cell lung cancer (SCLC).

Materials

A total of 134 SCLC patients who were treated with platinum as a first-line therapy were eligible for this study, including 51 patients with platinum resistance (PR) and 83 patients with platinum sensitivity (PS). The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were applied for feature selection and model construction. The selected texture features were calculated to obtain the radiomics score (Rad-score), and the predictive nomogram model was composed of the Rad-score and the clinical features selected by multivariate analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to assess the performance of the nomogram.

Results

The Rad-score was calculated using 10 radiomic features, and the resulting radiomics signature demonstrated good discrimination in both the training set (area under the curve [AUC], 0.727; 95% confidence interval [CI], 0.627–0.809) and the validation set (AUC, 0.723; 95% CI, 0.562–0.799). To improve diagnostic effectiveness, the Rad-score created a novel prediction nomogram by combining CA125 and CA72-4. The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.900; 95% CI, 0.844-0.947) and the validation set (AUC, 0.838; 95% CI, 0.534-0.735). The radiomics nomogram proved to be clinically beneficial based on decision curve analysis.

Conclusion

We developed and validated a radiomics nomogram model for predicting the response to platinum in SCLC patients. The outcomes of this model can provide useful suggestions for the development of tailored and customized second-line chemotherapy regimens.

Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion

Increasing evidences suggest that miRNAs play a key role in the occurrence and progression of many complex human diseases. Therefore, targeting dysregulated miRNAs with small molecule drugs in the clinical has become a new treatment. Nevertheless, it is high cost and time-consuming for identifying miRNAs-targeted with drugs by biological experiments. Thus, more reliable computational method for identification associations of drugs with miRNAs urgently need to be developed. In this study, we proposed an efficient method, called GNMFDMA, to predict potential associations of drug with miRNA by combining graph Laplacian regularization with non-negative matrix factorization. We first calculated the overall similarity matrices of drugs and miRNAs according to the collected different biological information. Subsequently, the new drug-miRNA association adjacency matrix was reformulated based on the K nearest neighbor profiles so as to put right the false negative associations. Finally, graph Laplacian regularization collaborative non-negative matrix factorization was used to calculate the association scores of drugs with miRNAs. In the cross validation, GNMFDMA obtains AUC of 0.9193, which outperformed the existing methods. In addition, case studies on three common drugs (i.e., 5-Aza-CdR, 5-FU and Gemcitabine), 30, 31 and 34 of the top-50 associations inferred by GNMFDMA were verified. These results reveal that GNMFDMA is a reliable and efficient computational approach for identifying the potential drug-miRNA associations.

Clinical Pharmacology of Radiotheranostics in Oncology

The combined use of diagnostic and therapeutic radioligands with the same molecular target, also known as theranostics, enables accurate patient selection, targeted therapy, and prediction of treatment response. Radioiodine, bone-seeking radioligands and norepinephrine analogs have been used for many years for diagnostic imaging and radioligand therapy of thyroid carcinoma, bone metastases, pheochromocytoma, paraganglioma, and neuroblastoma, respectively. In recent years, radiolabeled somatostatin analogs and prostate-specific membrane antigen ligands have shown clinical efficacy in the treatment of neuroendocrine tumors and prostate cancer, respectively. Several candidate compounds are targeting novel theranostic targets such as fibroblast activation protein, C-X-C chemokine receptor 4, and gastrin-releasing peptide receptor. In addition, several strategies to improve efficacy of radioligand therapy are being evaluated, including dosimetry-based dose optimization, multireceptor targeting, upregulation of target receptors, radiosensitization, pharmacogenomics, and radiation genomics. Design and evaluation of novel radioligands and optimization of dose and dose schedules, within the complex context of individualized multimodal cancer treatment, requires a multidisciplinary approach that includes clinical pharmacology. Significant increases in the use of these radiopharmaceuticals in routine oncological practice can be expected, which will have major impact on patient care as well as (radio)pharmacy utilization.

Sequencing of genes of drug response in tumor DNA and implications for precision medicine in cancer patients

Tumor DNA sequencing is becoming standard-of-care for patient treatment decisions. We evaluated genotype concordance between tumor DNA and genomic DNA from blood and catalogued functional effects of somatic mutations in 21 drug response genes in 752 solid tumor patients. Using a threshold of 10% difference between tumor and blood DNA variant allele fraction (VAF), concordance for heterogenous genotype calls was 78% and increased to 97.5% using a 30% VAF threshold. Somatic mutations were observed in all 21 drug response genes, and 44% of patients had at least one somatic mutation in these genes. In tumor DNA, eight patients had a frameshift mutation in CYP2C8, which metabolizes taxanes. Overall, somatic copy number losses were more frequent than gains, including for CYP2C19 and CYP2D6 which had the most frequent copy number losses. However, copy number gains in TPMT were more than four times as common as losses. Seven % of patients had copy number gains in ABCB1, a multidrug resistance transporter of anti-cancer agents. These results demonstrate tumor-only DNA sequencing might not be reliable to call germline genotypes of drug response variants.

Pharmacogenetics of Anticancer Drugs: Clinical Response and Toxicity

Cancer is the most challenging disease for medical professionals to treat. The factors underlying the complicated situation include anticancer drug-associated toxicity, non-specific response, low therapeutic window, variable treatment outcomes, development of drug resistance, treatment complications, and cancer recurrence. The remarkable advancement in biomedical sciences and genetics, over the past few decades, however, is changing the dire situation. The discovery of gene polymorphism, gene expression, biomarkers, particular molecular targets and pathways, and drug-metabolizing enzymes have paved the way for the development and provision of targeted and individualized anticancer treatment. Pharmacogenetics is the study of genetic factors having the potential to affect clinical responses and pharmacokinetic and pharmacodynamic behaviors of drugs. This chapter emphasizes pharmacogenetics of anticancer drugs and its applications in improving treatment outcomes, selectivity, toxicity of the drugs, and discovering and developing personalized anticancer drugs and genetic methods for prediction of drug response and toxicity.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development

Objective: Despite being a powerful tool to identify novel variants, genome-wide association studies (GWAS) are not sufficient to explain the biological function of variants. In this study, we aimed to elucidate at the gene level the biological mechanisms involved in gastric cancer (GC) development and to identify candidate drug target genes. Materials and methods: We conducted a systematic review for GWAS on GC following the PRISMA guidelines. Single nucleotide polymorphism (SNP)-level meta-analysis and gene-based analysis (GBA) were performed to identify SNPs and genes significantly associated with GC. Expression quantitative trait loci (eQTL), disease network, pathway enrichment, gene ontology, gene-drug, and chemical interaction analyses were conducted to elucidate the function of the genes identified by GBA. Results: A review of GWAS on GC identified 226 SNPs located in 91 genes. In the comprehensive GBA, 44 genes associated with GC were identified, among which 12 genes (THBS3, GBAP1, KRTCAP2, TRIM46, HCN3, MUC1, DAP3, EFNA1, MTX1, PRKAA1, PSCA, and ABO) were eQTL. Using disease network and pathway analyses, we identified that PRKAA, THBS3, and EFNA1 were significantly associated with the PI3K-Alt-mTOR-signaling pathway, which is involved in various oncogenic processes, and that MUC1 acts as a regulator in both the PI3K-Alt-mTOR and P53 signaling pathways. Furthermore, RPKAA1 had the highest number of interactions with drugs and chemicals. Conclusion: Our study suggests that PRKAA1, a gene in the PI3K-Alt-mTOR-signaling pathway, could be a potential target gene for drug development associated with GC in the future. Systematic Review Registration: website, identifier registration number.

Current Status and Trends of Research on Anthracycline-Induced Cardiotoxicity from 2002 to 2021: A Twenty-Year Bibliometric and Visualization Analysis

Anthracyclines constitute the cornerstone of numerous chemotherapy regimens for various cancers. However, the clinical application of anthracyclines is significantly limited to their dose-dependent cardiotoxicity. A comprehensive understanding of the current status of anthracycline-induced cardiotoxicity is necessary for in-depth research and optimal clinical protocols. Bibliometric analysis is widely applied in depicting development trends and tracking frontiers of a specific field. The present study is aimed at revealing the status and trends of anthracycline-induced cardiotoxicity during the past two decades by employing bibliometric software including R-bibliometric, VOSviewer, and CiteSpace. A total of 3504 publications concerning anthracycline-induced cardiotoxicity from 2002 to 2021 were collected from the Web of Science Core Collection database. Results showed significant growth in annual yields from 90 records in 2002 to 304 papers in 2021. The United States was the most productive country with the strongest collaboration worldwide in the field. Charles University in the Czech Republic was the institution that contributed the most papers, while 7 of the top 10 productive institutions were from the United States. The United States Department of Health and Human Services and the National Institutes of Health are the two agencies that provide financial support for more than 50% of sponsored publications. The research categories of included publications mainly belong to Oncology and Cardiac Cardiovascular Systems. The Journal of Clinical Oncology had a comprehensive impact on this research field with the highest IF value and many publications. Simunek Tomas from Charles University contributed the most publications, while Lipshultz Steven E. from the State University of New York possessed the highest H-index. In addition, the future research frontiers of anthracycline-induced cardiotoxicity might include early detection, pharmacogenomics, molecular mechanism, and cardiooncology. The present bibliometric analysis may provide a valuable reference for researchers and practitioners in future research directions.

Modern developments in germline pharmacogenomics for oncology prescribing

The integration of genomic data into personalized treatment planning has revolutionized oncology care. Despite this, patients with cancer remain vulnerable to high rates of adverse drug events and medication inefficacy, affecting prognosis and quality of life. Pharmacogenomics is a field seeking to identify germline genetic variants that contribute to an individual's unique drug response. Although there is widespread integration of genomic information in oncology, somatic platforms, rather than germline biomarkers, have dominated the attention of cancer providers. Patients with cancer potentially stand to benefit from improved integration of both somatic and germline genomic information, especially because the latter may complement treatment planning by informing toxicity risk for drugs with treatment-limiting tolerabilities and narrow therapeutic indices. Although certain germline pharmacogenes, such as TPMT, UGT1A1, and DPYD, have been recognized for decades, recent attention has illuminated modern potential dosing implications for a whole new set of anticancer agents, including targeted therapies and antibody-drug conjugates, as well as the discovery of additional genetic variants and newly relevant pharmacogenes. Some of this information has risen to the level of directing clinical action, with US Food and Drug Administration label guidance and recommendations by international societies and governing bodies. This review is focused on key new pharmacogenomic evidence and oncology-specific dosing recommendations. Personalized oncology care through integrated pharmacogenomics represents a unique multidisciplinary collaboration between oncologists, laboratory science, bioinformatics, pharmacists, clinical pharmacologists, and genetic counselors, among others. The authors posit that expanded consideration of germline genetic information can further transform the safe and effective practice of oncology in 2022 and beyond.

Transcriptomic Profile Analysis of Streptococcus mutans Response to Acmella paniculata Flower Extracts

Background

Acmella paniculata has been used as a traditional medicine to treat oral health diseases such as dental caries and periodontitis. Streptococcus mutans is a common bacterium that initiates dental caries at an early stage.

Aim

The aim of this study was to determine the mode of action of A. paniculata (extracts) against S. mutans growth.

Methods

Time-kill assay has been done to investigate the rate of kill and effectiveness of Acmella paniculata (AP) extracts against S. mutans growth. Phytochemical analysis was done to identify major compounds in AP extracts using gas chromatography mass spectrometry (GCMS). Scanning and transmission electron microscopy (SEM and TEM) have been done to observe the morphological changes of treated bacteria. Transcriptomic profile analysis has been done using Next Gene Sequencing.

Results

AP flower n-hexane (APFH) and AP flower dichloromethane (APFD) extracts acted as bactericidal agents after killing >3 log10 cfu/mL of S. mutans after 24 hours. Oleic and hexadecenoic acids were found to be the major compounds in APFD and APFH extracts, respectively. Photomicrographs from SEM and TEM of treated S. mutans show that the bacterial cell wall has been lysed and the cytoplasm content was decreased. Pathway analysis revealed that the APFD extract significantly affected biosynthesis peptidoglycan, gene expression, RNA processing, and macromolecule metabolism processes in S. mutans.

Conclusion

Data analysis revealed that multiple mechanisms of action were involved in antibacterial activity of A. paniculata extracts toward S. mutans.

Two polymorphic gene loci associated with treprostinil dose in pulmonary arterial hypertension

OBJECTIVE

Prostacyclin infusion for pulmonary arterial hypertension (PAH) is an effective therapy with varied dosing requirements and clinical response. The major aim of this study was to determine new biologically-based predictors of prostacyclin treatment response heterogeneity.

METHODS

Ninety-eight patients with hemodynamically defined PAH at two academic medical centers volunteered for registry studies. A stable dose of treprostinil was the quantitative phenotype for the genome-wide association study (GWAS). Candidate genes with the largest effect sizes and strongest statistical associations were further characterized with in silico and in-vitro assays to confirm mechanistic hypotheses. The clinical significance of these candidate predictors was assessed for mechanistically consistent physiologic effects in an independent cohort of patients.

RESULTS

GWAS identified three loci for association with P < 10-6. All three loci had clinically significant effect sizes. Specific single-nucleotide polymorphisms (SNPs) at two of the loci: rs11078738 in phosphoribosylformylglycinamidine synthase and rs10023113 in CAMK2D encoded sequence changes with clear predicted consequences. Production of the primary mediator of prostacyclin-induced vasodilation, cyclic AMP, was reduced in human cell lines by the missense variant rs11078738 (p.L621P). Located in the promoter of CAMK2D, the allele of rs10023113 associated with a higher treprostinil dose has higher ventricular transcription of CAMK2δ. At initial diagnostic catheterization in a separate cohort of patients, the same allele of rs10023113 was associated with elevated right mean atrial and ventricular diastolic pressures.

CONCLUSIONS

The quantitative phenotype of stable treprostinil dose identified two gene loci associated with pharmacodynamic response and right ventricular function in PAH worth further investigation.

Development of an optimized and generic cost-utility model for analyzing genome-guided treatment data

Economic evaluation is an integral component of informed public health decision-making in personalized medicine. However, performing economic evaluation assessments often requires specialized knowledge, expertise, and significant resources. To this end, developing generic models can significantly assist towards providing the necessary evidence for the cost-effectiveness of genome-guided therapeutic interventions, compared to the traditional drug treatment modalities. Here, we report a generic cost-utility analysis model, developed in R, which encompasses essential economic evaluation steps. Specifically, critical steps towards a comprehensive deterministic and probabilistic sensitivity analysis were incorporated in our model, while also providing an easy-to-use graphical user interface, which allows even non-experts in the field to produce a fully comprehensive cost-utility analysis report. To further demonstrate the model's reproducibility, two sets of data were assessed, one stemming from in-house clinical data and one based on previously published data. By implementing the generic model presented herein, we show that the model produces results in complete concordance with the traditionally performed cost-utility analysis for both datasets. Overall, this work demonstrates the potential of generic models to provide useful economic evidence for personalized medicine interventions.

Efficient Synthesis of 2,3'-Spirobi (Indolin)-2'-Ones and Preliminary Evaluation of Their Damage to Mitochondria in HeLa Cells

A novel formal (4 + 1) annulation between N-(o-chloromethyl)aryl amides and 3-chlorooxindoles through in situ generated aza-ortho-QMs with 3-chlorooxindoles is reported for the synthesis of a series of 2,3'-spirobi (indolin)-2'-ones in high yields. Under structured illumination microscopy, compound 3a is found to change the mitochondrial morphology and induce mitophagy pathway, which might then trigger mitophagy in cancer cells.

Clinically actionable genotypes for anticancer prescribing among >1500 patients with pharmacogenomic testing

BACKGROUND

In recent years, there has been increasing evidence supporting the role of germline pharmacogenomic factors predicting toxicity for anticancer therapies. Although somatic genomic data are used frequently in oncology care planning, germline pharmacogenomic testing is not. This study hypothesizes that comprehensive germline pharmacogenomic profiling could have high relevance for cancer care.

METHODS

Between January 2011 and August 2020, patients at the University of Chicago Medical Center were genotyped across custom germline pharmacogenomic panels for reasons unrelated to cancer care. Actionable anticancer pharmacogenomic gene/drug interactions identified by the FDA were defined including: CYP2C9 (erdafitinib), CYP2D6 (gefitinib), DPYD (5-fluorouracil and capecitabine), TPMT (thioguanine and mercaptopurine), and UGT1A1 (belinostat, irinotecan, nilotinib, pazopanib, and sacituzumab-govitecan hziy). The primary objective was to determine the frequency of individuals with actionable or high-risk genotypes across these 5 key pharmacogenes, thus potentially impacting prescribing for at least 1 of these 11 commonly prescribed anticancer therapies.

RESULTS

Data from a total of 1586 genotyped individuals were analyzed. The oncology pharmacogene with the highest prevalence of high-risk, actionable genotypes was UGT1A1, impacting 17% of genotyped individuals. Actionable TPMT and DPYD genotypes were found in 9% and 4% of patients, respectively. Overall, nearly one-third of patients genotyped across all 5 genes (161/525, 31%) had at least one actionable genotype.

CONCLUSIONS

These data suggest that germline pharmacogenomic testing for 5 key pharmacogenes could identify a substantial proportion of patients at risk with standard dosing, an estimated impact similar to that of somatic genomic profiling.

LAY SUMMARY

Differences in our genes may explain why some drugs work safely in certain individuals but can cause side effects in others. Pharmacogenomics is the study of how genetic variations affect an individual's response to medications. In this study, an evaluation was done for important genetic variations that can affect the tolerability of anticancer therapy. By analyzing the genetic results of >1500 patients, it was found that nearly one-third have genetic variations that could alter recommendations of what drug, or how much of, an anticancer therapy they should be given. Performing pharmacogenomic testing before prescribing could help to guide personalized oncology care.

The Gene-Drug Duality: Exploring the Pharmacogenomics of Indigenous Populations

While pharmacogenomic studies have facilitated the rapid expansion of personalized medicine, the benefits of these findings have not been evenly distributed. Genomic datasets pertaining to Indigenous populations are sorely lacking, leaving members of these communities at a higher risk of adverse drug reactions (ADRs), and associated negative outcomes. Australia has one of the largest Indigenous populations in the world. Pharmacogenomic studies of these diverse Indigenous Australian populations have been hampered by a paucity of data. In this article, we discuss the history of pharmacogenomics and highlight the inequalities that must be addressed to ensure equal access to pharmacogenomic-based healthcare. We also review efforts to conduct the pharmacogenomic profiling of chronic diseases among Australian Indigenous populations and survey the impact of the lack of drug safety-related information on potential ADRs among individuals in these communities.

Cancer: An unknown territory; rethinking before going ahead

Cancer is a disease of altered signaling and metabolism, causing uncontrolled division and survival of transformed cells. A host of molecules, factors, and conditions have been designated as underlying causes for the inception and progression of the disease. An enormous amount of data is available, system-wide interaction networks of the genes and proteins are generated over the years and have now reached up to a level of saturation, where we need to shift our focus to the more advanced and comprehensive methods and approaches of data analysis and visualization. Even with the availability of enormous literature on this one of the most pressing pathological conditions, a successful cure of the disease seems to be obscure. New treatment plans, like immunotherapy and precision medicine, are being employed for different studies. Nevertheless, their actual benefits to the patients would be known only after the evaluation of clinical data over the next few years. Therefore, we need to look at few fundamental challenges that should be addressed in more depth before we could devise better, rigorous, and comprehensive treatment plans and may successfully reach a possible cure of the disease. This article aims at bringing attention towards some fundamental gaps in our approach towards the disease that leads to failure in devising successful therapeutics.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

In the burgeoning field of personalized medicine, genetic variation is recognized as a major contributor to patients’ differential responses to drugs. Lymphoblastoid cell lines (LCLs) are a consistent and convenient representation of cells used for in vitro research. Human genome sequencing with LCLs can identify new genes that influence individuals’ drug responses, including the dose-response relationship, which describes the relationship between physiological response and the amount of exposure to a substance. In this work, we conduct high-throughput screening and genome-wide association mapping using 680 LCLs from the 1000 Genomes Project to identify new genes that influence individual response to 44 widely used anticancer drugs. We found the NQO1 gene to be associated with the dose-response of several drugs, namely arsenic trioxide, erlotinib, trametinib, and the paclitaxel + epirubicin combination, and performed follow-up analyses to better understand its functional role in drug response. Our results indicate NQO1 expression is correlated with increased drug resistance and provide some evidence that SNP rs1800566 influences drug response by altering protein activity for these four treatments. With further research, NQO1 has potential use as a therapeutic target, for example, suppressing NQO1 expression to increase sensitivity to particular drugs.

Genomewide Association Studies in Pharmacogenomics

The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

Genetic variants related to successful migraine prophylaxis with verapamil

BACKGROUND

Currently, there is no biologically based rationale for drug selection in migraine prophylactic treatment.

METHODS

To investigate the genetic variation underlying treatment response to verapamil prophylaxis, we selected 225 patients from a longitudinally established, deeply phenotyped migraine database (N = 5983), and collected uninterrupted quantitated verapamil treatment response data and DNA for these 225 cases. We recorded the number of headache days in the four weeks preceding treatment with verapamil and for four weeks, following completion of a treatment period with verapamil lasting at least five weeks. Whole-exome sequencing (WES) was applied to a discovery cohort consisting of 21 definitive responders and 14 definitive non-responders, and the identified single nucleotide polymorphisms (SNPs) showing significant association were genotyped in a separate confirmation cohort (185 verapamil treated patients). Statistical analysis of the WES data from the discovery cohort identified 524 SNPs associated with verapamil responsiveness (p < 0.01); among them, 39 SNPs were validated in the confirmatory cohort (n = 185) which included the full range of response to verapamil from highly responsive to not responsive.

RESULTS

Fourteen SNPs were confirmed by both percentage and arithmetic statistical approaches. Pathway and protein network analysis implicated myo-inositol biosynthetic and phospholipase-C second messenger pathways in verapamil responsiveness, emphasizing the earlier pathogenic understanding of migraine. No association was found between genetic variation in verapamil metabolic enzymes and treatment response.

CONCLUSION

Our findings demonstrate that genetic analysis in well-characterized subpopulations can yield important pharmacogenetic information pertaining to the mechanism of anti-migraine prophylactic medications.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice

BACKGROUND

Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77).

METHODS

Gradient-boosted trees were utilized as a novel feature selector to identify gene transcripts predictive of fulminant-like pulmonary tuberculosis. A novel attention-based multiple instance learning model for regression was used to predict selected genes' expression from whole-slide images. Gene expression predictions were shown to be sufficiently replicated to identify supersusceptible mice using gradient-boosted trees trained on ground truth gene expression data.

FINDINGS

The model was accurate, showing high positive correlations with ground truth gene expression on both cross-validation (n = 77, 0.63 ≤ ρ ≤ 0.84) and external testing sets (n = 33, 0.65 ≤ ρ ≤ 0.84). The sensitivity and specificity for gene expression predictions to identify supersusceptible mice (n=77) were 0.88 and 0.95, respectively, and for an external set of mice (n=33) 0.88 and 0.93, respectively.

IMPLICATIONS

Our methodology maps histopathology to gene expression with sufficient accuracy to predict a clinical outcome. The proposed methodology exemplifies a computational template for gene expression panels, in which relatively inexpensive and widely available tissue histopathology may be mapped to specific genes' expression to serve as a diagnostic or prognostic tool.

FUNDING

National Institutes of Health and American Lung Association.

Potential Impact of DPYD Variation on Fluoropyrimidine Drug Response in sub-Saharan African Populations

Cancer is a critical health burden in Africa, and mortality rates are rising rapidly. Treatments are expensive and often cause adverse drug reactions (ADRs). Fluoropyrimidine treatments can lead to severe toxicity events which have been linked to variants within the dihydropyrimidine dehydrogenase (DPYD) gene. There are clinical guidelines to improve safety outcomes of treatment, but these are primarily based on variants assessed in non-African populations. Whole genome sequencing data from the 1000 Genomes Project and the African Genome Variation Project were mined to assess variation in DPYD in eight sub-Saharan African populations. Variant functional annotation was performed with a series of bioinformatics tools to assess potential likelihood of deleterious impact. There were 29 DPYD coding variants identified in the datasets assessed, of which 25 are rare, and some of which are known to be deleterious. One African-specific variant (rs115232898-C), is common in sub-Saharan Africans (1-4%) and known to reduce the function of the dihydropyrimidine dehydrogenase enzyme (DPD), having been linked to cases of severe toxicity. This variant, once validated in clinical trials, should be considered for inclusion in clinical guidelines for use in sub-Saharan African populations. The rs2297595-C variant is less well-characterized in terms of effect, but shows significant allele frequency differences between sub-Saharan African populations (0.5-11.5%; p = 1.5 × 10^-4), and is more common in East African populations. This study highlights the relevance of African-data informed guidelines for fluorouracil drug safety in sub-Saharan Africans, and the need for region-specific data to ensure that Africans may benefit optimally from a precision medicine approach.

Response and Toxicity to Cytarabine Therapy in Leukemia and Lymphoma: From Dose Puzzle to Pharmacogenomic Biomarkers

Simple Summary

In this review, the authors propose a crosswise examination of cytarabine-related issues ranging from the spectrum of clinical activity and severe toxicities, through updated cellular pharmacology and drug formulations, to the genetic variants associated with drug-induced phenotypes. Cytarabine (cytosine arabinoside; Ara-C) in multiagent chemotherapy regimens is often used for leukemia or lymphoma treatments, as well as neoplastic meningitis. Chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. The individual variability in clinical response to Leukemia & Lymphoma treatments among patients appears to be associated with intracellular accumulation of Ara-CTP due to genetic variants related to metabolic enzymes. The review provides exhaustive information on the effects of Ara-C-based therapies, the adverse drug reaction will also be provided including bone pain, ocular toxicity (corneal pain, keratoconjunctivitis, and blurred vision), maculopapular rash, and occasional chest pain. Evidence for predicting the response to cytarabine-based treatments will be highlighted, pointing at their significant impact on the routine management of blood cancers.

Abstract

Cytarabine is a pyrimidine nucleoside analog, commonly used in multiagent chemotherapy regimens for the treatment of leukemia and lymphoma, as well as for neoplastic meningitis. Ara-C-based chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. Several studies suggest that the individual variability in clinical response to Leukemia & Lymphoma treatments among patients, underlying either Ara-C mechanism resistance or toxicity, appears to be associated with the intracellular accumulation and retention of Ara-CTP due to genetic variants related to metabolic enzymes. Herein, we reported (a) the latest Pharmacogenomics biomarkers associated with the response to cytarabine and (b) the new drug formulations with optimized pharmacokinetics. The purpose of this review is to provide readers with detailed and comprehensive information on the effects of Ara-C-based therapies, from biological to clinical practice, maintaining high the interest of both researcher and clinical hematologist. This review could help clinicians in predicting the response to cytarabine-based treatments.

Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines

The use of ex-vivo model systems to provide a level of forecasting for in-vivo characteristics remains an important need for cancer therapeutics. The use of lymphoblastoid cell lines (LCLs) is an attractive approach for pharmacogenomics and toxicogenomics, due to their scalability, efficiency, and cost-effectiveness. There is little data on the impact of demographic or clinical covariates on LCL response to chemotherapy. Paclitaxel sensitivity was determined in LCLs from 93 breast cancer patients from the University of North Carolina Lineberger Comprehensive Cancer Center Breast Cancer Database to test for potential associations and/or confounders in paclitaxel dose-response assays. Measures of paclitaxel cell viability were associated with patient data included treatment regimens, cancer status, demographic and environmental variables, and clinical outcomes. We used multivariate analysis of variance to identify the in-vivo variables associated with ex-vivo dose-response. In this unique dataset that includes both in-vivo and ex-vivo data from breast cancer patients, race (P = 0.0049) and smoking status (P = 0.0050) were found to be significantly associated with ex-vivo dose-response in LCLs. Racial differences in clinical dose-response have been previously described, but the smoking association has not been reported. Our results indicate that in-vivo smoking status can influence ex-vivo dose-response in LCLs, and more precise measures of covariates may allow for more precise forecasting of clinical effect. In addition, understanding the mechanism by which exposure to smoking in-vivo effects ex-vivo dose-response in LCLs may open up new avenues in the quest for better therapeutic prediction.

Role of HMOX1 Promoter Genetic Variants in Chemoresistance and Chemotherapy Induced Neutropenia in Children with Acute Lymphoblastic Leukemia

Whilst the survival rates of childhood acute lymphoblastic leukemia (ALL) have increased remarkably over the last decades, the therapy resistance and toxicity are still the major causes of treatment failure. It was shown that overexpression of heme oxygenase-1 (HO-1) promotes proliferation and chemoresistance of cancer cells. In humans, the HO-1 gene (HMOX1) expression is modulated by two polymorphisms in the promoter region: (GT)n-length polymorphism and single-nucleotide polymorphism (SNP) A(−413)T, with short GT repeat sequences and 413-A variants linked to an increased HO-1 inducibility. We found that the short alleles are significantly more frequent in ALL patients in comparison to the control group, and that their presence may be associated with a higher risk of treatment failure, reflecting the role of HO-1 in chemoresistance. We also observed that the presence of short alleles may predispose to develop chemotherapy-induced neutropenia. In case of SNP, the 413-T variant co-segregated with short or long alleles, while 413-A almost selectively co-segregated with long alleles, hence it is not possible to determine if SNPs are actually of phenotypic significance. Our results suggest that HO-1 can be a potential target to overcome the treatment failure in ALL patients.

Implementation of pharmacogenomic testing in oncology care (PhOCus): study protocol of a pragmatic, randomized clinical trial

BACKGROUND

Many cancer patients who receive chemotherapy experience adverse drug effects. Pharmacogenomics (PGx) has promise to personalize chemotherapy drug dosing to maximize efficacy and safety. Fluoropyrimidines and irinotecan have well-known germline PGx associations. At our institution, we have delivered PGx clinical decision support (CDS) based on preemptively obtained genotyping results for a large number of non-oncology medications since 2012, but have not previously evaluated the utility of this strategy for patients initiating anti-cancer regimens. We hypothesize that providing oncologists with preemptive germline PGx information along with CDS will enable individualized dosing decisions and result in improved patient outcomes.

METHODS

Patients with oncologic malignancies for whom fluoropyrimidine and/or irinotecan-inclusive therapy is being planned will be enrolled and randomly assigned to PGx and control arms. Patients will be genotyped in a clinical laboratory across panels that include actionable variants in UGT1A1 and DPYD. For PGx arm patients, treating providers will be given access to the patient-specific PGx results with CDS prior to treatment initiation. In the control arm, genotyping will be deferred, and dosing will occur as per usual care. Co-primary endpoints are dose intensity deviation rate (the proportion of patients receiving dose modifications during the first treatment cycle), and grade ⩾3 treatment-related toxicities throughout the treatment course. Additional study endpoints will include cumulative drug dose intensity, progression-free survival, dosing of additional PGx supportive medications, and patient-reported quality of life and understanding of PGx.

DISCUSSION

Providing a platform of integrated germline PGx information may promote personalized chemotherapy dosing decisions and establish a new model of care to optimize oncology treatment planning.

Updates on "Cancer Genomics and Epigenomics"

The field of "Cancer Genomics and Epigenomes" has been widely investigated for their involvement in cancer to understand the basic processes of different malignancies. The aggregation of genetic and epigenetic alterations also displays a wide range of heterogeneity making it quite necessary to develop personalized treatment strategies. The complex interplay between DNA methylation and chromatin dynamics in malignant cells is one of the major epigenetic mechanisms that lead to gene activation and repression. Hence, each tumor needs to be fully characterized to satisfy the ideas of personalized treatment strategies. The present article addresses various aspects of genome characterization methods and their potential role in the field of cancer genomics and epigenomics.

Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma

BACKGROUND

Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on.

OBJECTIVE

We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication.

METHODS

PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail.

RESULTS

In entity recognition tasks, the Bidirectional Encoder Representations from Transformers-conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available.

CONCLUSIONS

We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data.

Predictive model for risk of gastric cancer using genetic variants from genome-wide association studies and high-evidence meta-analysis

Genome-wide association studies (GWAS) have identified some single nucleotide polymorphisms (SNPs) associated with the risk of gastric cancer (GCa). However, currently, there is no published predictive model to assess the risk of GCa. In the present study, risk-associated SNPs derived from GWAS and large meta-analyses were selected to construct a predictive model to assess the risk of GCa. A total of 1115 GCa cases and 1172 controls from the eastern Chinese population were included. Logistic regression models were used to identify SNPs that correlated with the risk of GCa. A predictive model to assess the risk of GCa was established by receiver operating characteristic curve analysis. Multifactor dimensionality reduction (MDR) and classification and regression tree (CART) were applied to calculate the effect of high-order gene-environment interactions on risk of the cancer. A total of 42 SNPs were selected for further analysis. The results revealed that ASH1L rs80142782, PKLR rs3762272, PRKAA1 rs13361707, MUC1 rs4072037, PSCA rs2294008, and PLCE1 rs2274223 polymorphisms were associated with a risk of GCa. The area under curve considering both genetic factors and BMI was 3.10% higher than that of BMI alone. MDR analysis revealed that rs13361707 and rs4072307 variants and BMI had interaction effects on susceptibility to GCa, with the highest predictive accuracy (61.23%) and cross-validation consistency (100/100). CART analysis also supported this interaction model that non-overweight status and a six SNP panel could synergistically increase the susceptibility to GCa. The six SNP panel for predicting the risk of GCa may provide new tools for prevention of the cancer based on GWAS and large meta-analyses derived genetic variants.

Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier

BACKGROUND

Inter-individual differences in dihydropyrimidine dehydrogenase (DPYD encoding DPD) and thiopurine S-methyltransferase (TPMT) activity are important predictors for fluoropyrimidine and thiopurine toxicity. While several variants in these genes are known to decrease enzyme activities, many additional genetic variations with unclear functional consequences have been identified, complicating informed clinical decision-making in the respective carriers.

METHODS

We used a novel pharmacogenetically trained ensemble classifier to analyse DPYD and TPMT genetic variability based on sequencing data from 138,842 individuals across eight populations.

RESULTS

The algorithm accurately predicted in vivo consequences of DPYD and TPMT variants (accuracy 91.4% compared to 95.3% in vitro). Further analysis showed high genetic complexity of DPD deficiency, advocating for sequencing-based DPYD profiling, whereas genotyping of four variants in TPMT was sufficient to explain >95% of phenotypic TPMT variability. Lastly, we provided population-scale profiles of ethnogeographic variability in DPD and TPMT phenotypes, and revealed striking interethnic differences in frequency and genetic constitution of DPD and TPMT deficiency.

CONCLUSION

These results provide the most comprehensive data set of DPYD and TPMT variability published to date with important implications for population-adjusted genetic profiling strategies of fluoropyrimidine and thiopurine risk factors and precision public health.

Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10⁻³ for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

Mechanisms of cancer stem cell therapy

Cancer stem cells (CSCs) are responsible for carcinogenesis and tumorigenesis and are involved in drug and radiation resistance, metastasis, tumor relapse and initiation. Remarkably, they have other abilities such as inheritance of self-renewal and de-differentiation. Hence, targeting CSCs is considered a potential anti-cancer therapeutic strategy. Recent advances in the identification of biomarkers to recognize CSCs and the development of new techniques to evaluate tumorigenic and carcinogenic roles of CSCs are instrumental to this approach. Elucidation of signaling pathways that regulate CSCs colony progression and drug resistance are critical in establishing effective targeted therapies. CSCs play a central key role in immunomodulation, immune evasion and effector immunity, which alters immune system balancing. These include mTOR, SHH, NOTCH and Wnt/β-catering in cancer progression. In this review article, we discuss the importance of these CSCs pathways in cancer therapy.

Decreasing Cost and Decreasing Length of Stay After Implementation of Updated High-Dose Methotrexate Discharge Criteria

PURPOSE

High-dose methotrexate (HD-MTX) is commonly used for the treatment of osteosarcoma or for CNS involvement in lymphoproliferative neoplasms. It is often given in the inpatient setting because of monitoring requirements after administration. We conducted a process improvement initiative to change our institutional discharge criteria for HD-MTX from 0.05 µmol/L to ≤ 0.1 µmol/L to reduce cost and length of stay (LOS) for this patient population.

METHODS

After an assessment of drivers of LOS among patients receiving HD-MTX, we identified discharge criteria as an actionable factor. We developed a workflow to discharge patients with 3 days of oral leucovorin and sodium bicarbonate when the methotrexate level reached ≤ 0.1 µmol/L. Patient demographics, chemotherapy regimen, cycle, dose, and LOS data were collected for a 7-month period before and a 4-month period after the intervention. Cost savings were estimated on the basis of the daily cost of a hospital bed at the institution.

RESULTS

Mean LOS for the pre-intervention and postintervention group was 4.84 days (n = 49) and 3.67 days (n = 42), respectively, resulting in a 24.4% reduction in LOS, with a mean ratio of 0.756 (95% CI, 0.615 to 0.927; P = .007). Reduced LOS resulted in a decrease in cost of $1,828.73 per admission, with a 4-month savings of $76, 806.56 and projected annualized savings of $230,419.67. No patient experienced complications because of the change in discharge criteria.

CONCLUSION

Liberalizing discharge criteria for HD-MTX was feasible and safe and reduced cost. Additional efforts to reduce LOS for elective chemotherapy admissions or to safely transition some of these complex regimens to the home setting are currently underway at our institution.

ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification

Motivation

Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process.

Results

Here, we present ClinOmicsTrail^bc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor’s main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrail^bc’s rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrail^bc can support breast cancer precision medicine. ClinOmicsTrail^bc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results.

Availability and implementation

ClinOmicsTrail^bc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

miR-1262 Transcriptionally Modulated by an Enhancer Genetic Variant Improves Efficiency of Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors in Advanced Lung Adenocarcinoma

Advanced nonsmall-cell lung cancer (NSCLC) patients with mutated epidermal growth factor receptor (EGFR) can remarkably benefit from target therapy of EGFR-tyrosine kinase inhibitors (TKIs). However, increasing drug sensitivity and improving outcomes of NSCLC patients to EGFR-TKI therapy remains a challenge. Several studies have shown a link between microRNAs and drug resistance in cancer. In this study, we hypothesized that the rs12740674 single nucleotide polymorphism in the enhancer of miR-1262 may affect its expression, which may impact the outcome of NSCLC patients treated with EGFR-TKIs. The rs12740674 polymorphism was genotyped in two independent cohorts, including 319 EGFR-TKI treated stage IIIB/IV NSCLC patients. The allele-specific regulation on miR-1262 transcription by rs12740674 and impacts of miR-1262 on gefitinib sensitivity were evaluated in vitro and in vivo. Cox regression analyses indicated that the rs12740674 T allele was significantly associated with short survival time in both cohorts (p < 0.05). Luciferase assays demonstrated that the rs12740674 T allelic enhancer showed weaker capability to promote miR-1262 transcription compared with the C allelic enhancer, which may be due to reduced transcription factor binding according to electrophoretic mobility shift assays. Furthermore, significantly decreased miR-1262 expression in NSCLC and nontumor lung tissues of T allele carriers was observed compared with levels in C allele carriers. Moreover, miR-1262 expression enhanced the anticancer effects of gefitinib on NSCLC cells. Our data indicate that miR-1262 may be a potential therapeutic target for NSCLC.

Complexities of pharmacogenomic interactions in cancer

Genetic and genomic alterations drive cancer development. However, they may also constitute vulnerabilities, including increased drug sensitivity, which could be harnessed for precision medicine purposes. We discuss the highly complex pharmacogenomic interactions that are beginning to be disentangled and hurdles that may need to be overcome before cancer patients could benefit.

Pharmaco-Geno-Proteo-Metabolomics and Translational Research in Cancer

The diagnosis, prognosis and treatment of cancer has had a great improvement due to the "omics" technologies such as genomics, proteomics, epigenomics, pharmacogenomics, and metabolomics. The technological progress of these technologies has allowed precision medicine to become a clinical reality. The study of different biomolecules such as DNA, RNA and proteins has helped to detect alterations in genes, changes in gene expression profiles and loss or gain of protein function, which allows us to make associations and better understand the cancer biology. Data obtained from different "omics" technologies gives a complementary spectrum of information that helps us to understand and unveil new information for a better diagnosis, prognosis, prediction of new molecular targets of anticancer therapies, etc. This chapter presents a general landscape of the interaction between the Pharmaco-Geno-Proteo-Metabolomic and translational medicine research in cancer.