Pharmacogenomic Biomarkers Research Articles

Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1,ERICH3,AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

Pharmacogenomics education in medical and pharmacy schools: conclusions of a global survey.

Aim: The need for pharmacogenomic education is becoming more and more urgent. Our aim was to evaluate the progress in pharmacogenomics education since then, and to put forward further recommendations. Methods: A survey was sent to 248 schools of medicine, pharmacy, nursing and health professions around the world. Results: The majority of the study programs (87%) include pharmacogenomics education, which is generally taught as part of the pharmacology curriculum. On average, educators and teachers have selected appropriate and highly relevant pharmacogenomics biomarkers to include in their teaching programs. Conclusions: Based on the results, we can conclude that the state of pharmacogenomics education at the surveyed universities has improved substantially since 2005.

DGM1 may serve as a novel genetic biomarker of response to enzastaurin in glioblastoma.

2023 Background: Despite countless clinical trials being conducted, little has changed over the last decade in the chemotherapies available for glioblastoma (GBM) with survival remaining poor. Meaningful advances in treating this deadly malignancy may rely on precision medicine. We discovered a novel pharmacogenomic biomarker for enzastaurin (enz) in treating lymphoma (lymph). We evaluated if this biomarker can be used to predict enz response in GBM. Methods: Biomarker discovery was performed by a genome-wide screen using DNA extracted from blood samples from a ph 3 enz lymph trial and confirmed in an independent ph 2 enz lymph trial. The biomarker was then evaluated for its predictability in GBM using the archived DNA samples from a prior ph 1/2 enz GBM trial. Results: A novel biomarker, Denovo Genomic Marker 1 (DGM1), a germline polymorphism on chromosome 8, was found to be highly correlated with response to enz in the two lymph trials. Using DNA extracted from blood of pts from the single-arm ph 1/2 study of newly diagnosed GBM receiving enz added to radiation and temozolomide (tmz), we found median OS for DGM1+ pts treated with enz was 18 mon vs 12.8 mon for DGM1- pts, HR (95% CI) 0.68 (0.25, 1.81), p = 0.12. In addition, we found pts in the GBM study receiving a mean daily dose of enz ≥ 245 mg had an OS of 19.8 mon vs 14.9 mon for pts receiving a mean daily dose of &lt; 245 mg [HR (95% CI) 0.55 (0.34, 0.90)]; enz 500 mg/day was used in the lymph studies. Conclusions: These data are supportive of DGM1 as a potentially predictive biomarker for enz response in both lymph and GBM. There is an ongoing biomarker-driven pivotal ph 3 study in lymph at 500 mg/day, and DGM1 in GBM will be further evaluated in a planned randomized ph 2b study in newly diagnosed GBM with 500 mg/day of enz in combination with tmz.

Optimizing Patient Selection to Maximize Drug Efficacy: the Expanding Role of Pharmacogenomics in the Clinical Development of Pembrolizumab for the Treatment of Non-small Cell Lung Cancer

Pembrolizumab (MK-3475) is a potent and highly selective humanized monoclonal antibody of the immunoglobulin G4κ class directed against the immune checkpoint programmed cell death protein-1 (PD-1). Binding to PD-1 prevents its interaction with natural ligands and allows for the reactivation of the immune response against cancer cells. The list of approved indications of pembrolizumab is fast expanding, including its use as first-line treatment of metastatic non–small cell lung cancer (NSCLC), which is a complex and evolving disease. Pharmacogenomics significantly contributed to understanding this complexity, allowing for the identification of molecular biomarkers and novel pharmacologic targets. This approach has delivered more effective and less toxic drugs for advanced NSCLC. In our opinion, pharmacogenomics played a key role in the approval of pembrolizumab as frontline therapy for NSCLC with high expression of the PD-1 ligand, which occurs in ∼30% of patients. Moreover, an analysis conducted on the ongoing clinical trials sponsored by the drug's patent holder shows that the characterization and validation of additional pharmacogenomic biomarkers of response has the potential to extend the frontline clinical use of pembrolizumab in NSCLC.

Integrating Next-Generation Sequencing in the Clinical Pharmacogenomics Workflow.

Pharmacogenomics has been recognized as a fundamental tool in the era of personalized medicine with up to 266 drug labels, approved by major regulatory bodies, currently containing pharmacogenomics information. Next-generation sequencing analysis assumes a critical role in personalized medicine, providing a comprehensive profile of an individual’s variome, particularly that of clinical relevance, comprising of pathogenic variants and pharmacogenomic biomarkers. Here, we propose a strategy to integrate next-generation sequencing into the current clinical pharmacogenomics workflow from deep resequencing to pharmacogenomics consultation, according to the existing guidelines and recommendations.

Pharmacogenomics of Multiple Sclerosis: A Systematic Review.

Background: Over the past two decades, various novel disease-modifying drugs for multiple sclerosis (MS) have been approved. However, there is high variability in the patient response to the available medications, which is hypothesized to be partly attributed to genetics.Objectives: To conduct a systematic review of the current literature on the pharmacogenomics of MS therapy.Methods: A systematic literature search was conducted using PubMed/MEDLINE database searching for articles investigating a role of genetic variation in response to disease-modifying MS treatments, published in the English language up to October 9th, 2018. PRISMA guidelines for systematic reviews were applied. Studies were included if they investigated response or nonresponse to MS treatment defined as relapse rate, by expanded disability status scale score or based on magnetic resonance imaging. The following data were extracted: first author's last name, year of publication, PMID number, sample size, ethnicity of patients, method, genes, and polymorphisms tested, outcome, significant associations with corresponding P-values and confidence intervals, response criteria, and duration of the follow-up period.Results: Overall, 48 articles published up to October 2018, evaluating response to interferon-beta, glatiramer acetate, mitoxantrone, and natalizumab, met our inclusion criteria and were included in this review. Among those, we identified 42 (87.5%) candidate gene studies and 6 (12.5%) genome-wide association studies. Existing pharmacogenomic evidence is mainly based on the results of individual studies, or on results of multiple studies, which often lack consistency. In recent years, hypothesis-free approaches identified novel candidate genes that remain to be validated. Various study designs, including the definition of clinical response, duration of the follow-up period, and methodology as well as moderate sample sizes, likely contributed to discordances between studies. However, some of the significant associations were identified in the same genes, or in the genes involved in the same biological pathways.Conclusions: At the moment, there is no available clinically actionable pharmacogenomic biomarker that would enable more personalized treatment of MS. More large-scale studies with uniform design are needed to identify novel and validate existing pharmacogenomics findings. Furthermore, studies investigating associations between rare variants and treatment response in MS patients, using next-generation sequencing technologies are warranted.

A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications.

Recent advances in next-generation sequencing and computational technologies have enabled routine analysis of large-scale single-cell ribonucleic acid sequencing (scRNA-seq) data. However, scRNA-seq technologies have suffered from several technical challenges, including low mean expression levels in most genes and higher frequencies of missing data than bulk population sequencing technologies. Identifying functional gene sets and their regulatory networks that link specific cell types to human diseases and therapeutics from scRNA-seq profiles are daunting tasks. In this study, we developed a Component Overlapping Attribute Clustering (COAC) algorithm to perform the localized (cell subpopulation) gene co-expression network analysis from large-scale scRNA-seq profiles. Gene subnetworks that represent specific gene co-expression patterns are inferred from the components of a decomposed matrix of scRNA-seq profiles. We showed that single-cell gene subnetworks identified by COAC from multiple time points within cell phases can be used for cell type identification with high accuracy (83%). In addition, COAC-inferred subnetworks from melanoma patients’ scRNA-seq profiles are highly correlated with survival rate from The Cancer Genome Atlas (TCGA). Moreover, the localized gene subnetworks identified by COAC from individual patients’ scRNA-seq data can be used as pharmacogenomics biomarkers to predict drug responses (The area under the receiver operating characteristic curves ranges from 0.728 to 0.783) in cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. In summary, COAC offers a powerful tool to identify potential network-based diagnostic and pharmacogenomics biomarkers from large-scale scRNA-seq profiles. COAC is freely available at https://github.com/ChengF-Lab/COAC.

Association between SLCO1B1 rs4149056 and tegafur-uracil-induced hepatic dysfunction in breast cancer.

Aim: The aim of this study was to identify pharmacogenomic biomarkers to predict tegafur-uracil (UFT)-induced liver dysfunction. Patients & methods:A total of 68patients, who were administered UFT, were evaluated using a two-step pharmacogenomics analysis. Results: The first screening revealed the association between five SNPs and UFT-induced hepatic dysfunction. In the second step, SLCO1B1 (rs4149056) was found to be the only SNP associated with UFT treatment-related elevation of aspartate aminotransferase (odds ratio: C/C vs T/T=7.8, C/T vs T/T=5.7; p=0.037) and alanine transaminase (odds ratio: C/C vs T/T=12.2, C/T vs T/T=4.1; p=0.034) levels. Conclusion:TheSLCO1B1 polymorphisms are possible predictors of UFT treatment-related hepatic dysfunction.

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity.

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

Colorectal Cancer Biomarkers in the Era of Personalized Medicine.

The 5-year survival probability for patients with metastatic colorectal cancer has not drastically changed over the last several years, nor has the backbone chemotherapy in first-line disease. Nevertheless, newer targeted therapies and immunotherapies have been approved primarily in the refractory setting, which appears to benefit a small proportion of patients. Until recently, rat sarcoma (RAS) mutations remained the only genomic biomarker to assist with therapy selection in metastatic colorectal cancer. Next generation sequencing has unveiled many more potentially powerful predictive genomic markers of therapy response. Importantly, there are also clinical and physiologic predictive or prognostic biomarkers, such as tumor sidedness. Variations in germline pharmacogenomic biomarkers have demonstrated usefulness in determining response or risk of toxicity, which can be critical in defining dose intensity. This review outlines such biomarkers and summarizes their clinical implications on the treatment of colorectal cancer. It is critical that clinicians understand which biomarkers are clinically validated for use in practice and how to act on such test results.

Intensive first-line FIr-C/FOx-C association of triplet chemotherapy plus cetuximab in RAS wild-type metastatic colorectal cancer patients: preliminary phase II data and prediction of individual limiting toxicity syndromes by pharmacogenomic biomarkers.

Background:Intensive triplet chemotherapy/bevacizumab significantly increased metastatic colorectal cancer (MCRC) outcome. This phase II study investigated the safety/activity of FIr-C/FOx-C triplet/cetuximab (CET) in first-line RAS wild-type and the prediction of individual limiting toxicity syndromes (LTS) by pharmacogenomic biomarkers.Methods:A Simon two-step design was used: p0 70%, p1 85%, power 80%, α5%, β20%; projected objective response rate (ORR) I step 14/19. FIr-C/FOx-C: 5-fluorouracil (5-FU) 12h-timed flat infusion 900 mg/m2 d1–2, 8–9, 15–16, 22–23; irinotecan (CPT-11) 160 mg/m2 d1 and 15, oxaliplatin 80 mg/m2 d8 and 22; CET 400mg/m2 then 250 mg/m2 d1, 8, 15, 22; every 28 days. Toxicity, and individual LTS were evaluated, compared by a Chi-square test; and activity/efficacy by log-rank. 5-FU/CPT-11 pharmacogenomic biomarkers, 5-FU degradation rate (5-FUDR), single nucleotide polymorphisms (SNPs) ABCB1, CYP3A4, DYPD, UGT1A1 were evaluated in patients with LTS and at a recommended dose.Results:A total of 29 patients <75 years, with a primary/intermediate Cumulative Index Rating Scale were enrolled; the median age was 59 years; there were 7 young-elderly (yE; 24%). Recommended CPT-11/5-FU doses were 120/750 mg/m2. In the intent-to-treat analysis, the ORR was 58.6%. The primary endpoint was met in patients who received the planned three treatment cycles: the objective response (OR) was 14/18 (78%). At a median follow up of 18 months, progression-free survival (PFS) was 12, and overall survival (OS) was 23 months. At the recommended doses (received dose intensity >80%), grade 3–4 toxicities were: diarrhea 23%, asthenia 15%, vomiting 8%, hypertransaminasemy 8%; LTS 19 (65.5%), with 83% in yE patients. LTS prevalently multiple (ms) versus single site were 59% versus 7% (p = 0.006). The prevalence of reduced FUDR was 56%, SNPs CYP3A4 22%, UGT1A1 71%, and of >2 positive pharmacogenomics biomarkers was 78%, prevalently reported in patients who developed gastrointestinal LTS.Conclusions:FIr-C/FOx-C is highly active and tolerable at recommended doses in non-elderly RAS wild-type MCRC patients. LTS provided an evaluation of the toxicity burden in individual patients. Reduced FUDR, CYP3A4, and UGT1A1 SNPs may predict individual LTS-ms in patients at risk of limiting gastrointestinal toxicity.Trial registration:The trial was registered at Osservatorio Nazionale sulla Sperimentazione Clinica dei Medicinali (OsSC) Agenzia Italiana del Farmaco (AIFA) Numero EudraCT 2009-016793-32.

Opportunities and challenges of implementing Pharmacogenomics in cancer drug development.

Cancer drug development is a time and resources consuming process. Around 90% of drugs entering clinical trials fail due to lack of efficacy and/or safety issues, more often after conspicuous research and economic efforts. Part of the discarded drugs might be beneficial only in a subgroup of the study patients, and some adverse events might be prevented by identifying those patients more vulnerable to toxicities. The implementation of pharmacogenomic biomarkers allows the categorization of patients, to predict efficacy and toxicity and to optimize the drug development process. Around seventy FDA approved drugs currently present one or more genetic biomarker to keep in consideration, and with the progress of Precision Medicine tailoring therapies on individuals’ genomic landscape promises to become a new standard of cancer care. In the current article we review the role of pharmacogenomics in cancer drug development, underlying the advantages and challenges of their implementation.

Gap between the US and Japan in coverage of pharmacogenomic biomarkers by health insurance programs: More coverage is needed in Japan

In this study, we aimed to understand the gap in coverage of pharmacogenomic (PGx) biomarkers between Japan and the US. PGx biomarkers (1) in the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines; (2) that are CPIC level A or B; or (3) have US Food and Drug Administration (FDA)-approved drug labels, were determined. Subsequently, their coverage by US health insurance companies and the National Health Insurance (NHI) in Japan was investigated. We identified the top six health insurance companies with the largest market shares in the US and investigated the coverage for the PGx biomarkers by these health insurers, Medicare, Medicaid, and the NHI in Japan. We found that 19.9% of these biomarkers are covered by the six companies (10.0%, the CPIC guidelines; 25.1%, the FDA-approved drug labels). The coverage of somatic and germline biomarkers was respectively 86.8% and 8.5% in the US and 56.3% and 0.6% in Japan. A few germline PGx biomarkers are covered both in Japan and the US, but the coverage of both somatic and germline biomarkers was lower in Japan. Therefore, more coverage should be considered to improve patient outcomes after prescribing medications in Japan.

NOS3 895G>T and CBR3 730G>A Are Associated with Recurrence Risk in Non-Muscle-Invasive Bladder Cancer with Intravesical Instillations of THP

Objectives: To analyze the correlation between pharmacogenomic biomarkers and the efficacy of pirarubicin (THP, also named 4’-O-tetrahydropyranyl-adriamycin) and to explore potential associations of individual genetic backgrounds with the clinical outcomes of non-muscle-invasive bladder cancer (NMIBC) patients. Methods: Between July 2003 and June 2011, a total of 91 patients were treated with transurethral resection (TUR) of the bladder tumor and were histopathologically confirmed to have NMIBC. Patients received an immediate instillation and maintenance therapy with THP. All patients underwent follow-up for recurrence. We genotyped 13 single nucleotide polymorphisms (SNPs) from blood and saliva DNA samples of all patients. Results: The associations of patients’ genotypes with tumor recurrence risks were analyzed by survival analysis. A total of 16 (17.6%) of the 91 patients with NMIBC had tumor recurrences with a median follow-up of 17 months (range, 2–83 months). We confirmed the effect of the European Organization for Research and Treatment of Cancer (EORTC) risk score for predicting tumor recurrence (p = 0.002, log-rank test). We adjusted for the EORTC score and found that 2 SNPs, NOS3 895G>T (rs1799983) (p = 0.02, HR = 4.32, 95% CI, 1.30–14.39, GT+TT vs. GG) and CBR3 730G>A (rs1056892) (p = 0.04, HR = 2.57, 95% CI, 1.07–6.18, GA+AA vs. GG), were significantly associated with a higher recurrence risk after TUR and instillations of THP in NMIBC patients. Conclusions: Our results suggest that NOS3 895G>T and CBR3 730G>A are genetic markers that can be used to predict tumor recurrence in NMIBC patients receiving intravesical instillations of THP. The effects of those 2 SNPs are independent of the EORTC scores. Further studies with larger sample sizes and longer follow-ups are needed to confirm our results.

Individualized Genetic Network Analysis Reveals New Therapeutic Vulnerabilities in Cancer

Background: Tumor-specific genomic alterations derived from multi-center cancer genome projects allow systematic identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual tumors. Methods: In this study, we develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations and RNA-Seq profiles derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas (TCGA). Findings: Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes in cancer populations, experimentally validated cancer genes, chromosome regulatory factors, and DNA repair genes, as compared to human essential genes identified by CRISPR-Cas9 screenings of cancer cell lines. We find that genes identified in the cancer type-specific genetic subnetworks by INCM are significantly enriched in established cancer pathways, and the INCM-predicted putative genetic interactions (e.g., BCL2L1-HRAS and BACH2-KRAS) are correlated with patient survival. By analyzing drug pharmacogenomics profiles across over 1,000 cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53 and KRAS-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents in cancer cell lines. Finally, drug-target network analysis reveals multiple potentially druggable genetic interactions (e.g., CETN2-CDK4 and PIK3CA-PTEN) by targeting tumor vulnerabilities. In summary, this study offers a powerful network-based methodology for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine. Funding: This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K99HL138272 and R00HL138272 to F.C. This work has been also funded in whole or in part with Federal funds from the Frederick National Laboratory for Cancer Research, National Institutes of Health, under contract HHSN261200800001E. This research was supported (in part) by the Intramural Research Program of NIH, Frederick National Lab, Center for Cancer Research. Declaration of Interest: The authors declare no competing financial interest.

An opportunity for clinical pharmacology trained physicians to improve patient drug safety: A retrospective analysis of adverse drug reactions in teenagers.

Background: Adverse drug reactions (ADRs) are a major cause of hospital admissions, prolonged hospital stays, morbidity, and drug-related mortality. In this study, we sought to identify the most frequently reported medications and associated side effects in adolescent-aged patients in an effort to prioritize clinical pharmacology consultation efforts for hospitals seeking to improve patient safety. Methods: Quarterly reported data were obtained from the United States Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2014 and ending in the third quarter of 2017. We then used the GeneCards database to map the pharmacogenomic biomarkers associated with the most reported FAERS drugs. Data homogenization and statistics analysis were all conducted in R for statistical programming. Results: We identified risperidone (10.64%) as the compound with the most reported ADRs from all reported cases. Males represented 90.1% of reported risperidone cases with gynecomastia being the most reported ADR. Ibuprofen OR=188 (95% CI, 105.00 - 335.00) and quetiapine fumarate OR=116 (95% CI, 48.40 - 278.00) were associated with the highest odds of completed suicide in teenagers. Ondansetron hydrochloride OR=7.12 (95% CI, 1.59 - 31.9) resulted in the highest odds of pneumothorax. Lastly, olanzapine (8.96%) represented the compound with the most reported drug-drug interactions cases, while valproic acid OR=221 (95% CI, 93.900 - 522.00) was associated with the highest odds of drug-drug interactions. Conclusion: Despite any data limitations, physicians prescribing risperidone in males should be aware of the high rates of adverse drug events and an alternative psychotropic should be considered in male patients. Further, patients with a history of pneumothorax or genetically predisposed to pneumothorax should be considered for an alternative antiemetic to ondansetron hydrochloride, due to increased odds associated with the drug and adverse event.

An opportunity for clinical pharmacology trained physicians to improve patient drug safety: A retrospective analysis of adverse drug reactions in teenagers

Background: Adverse drug reactions (ADRs) are a major cause of hospital admissions, prolonged hospital stays, morbidity, and drug-related mortality. In this study, we sought to identify the most frequently reported medications and associated side effects in adolescent-aged patients in an effort to prioritize clinical pharmacology consultation efforts for hospitals seeking to improve patient safety. Methods: Quarterly reported data were obtained from the United States Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2014 and ending in the third quarter of 2017. We then used the GeneCards database to map the pharmacogenomic biomarkers associated with the most reported FAERS drugs. Data homogenization and statistics analysis were all conducted in R for statistical programming. Results: We identified risperidone (10.64%) as the compound with the most reported ADRs from all reported cases. Males represented 90.1% of reported risperidone cases with gynecomastia being the most reported ADR. Ibuprofen OR=188 (95% CI, 105.00 – 335.00) and quetiapine fumarate OR=116 (95% CI, 48.40 – 278.00) were associated with the highest odds of completed suicide in teenagers. Ondansetron hydrochloride OR=7.12 (95% CI, 1.59 – 31.9) resulted in the highest odds of pneumothorax. Lastly, olanzapine (8.96%) represented the compound with the most reported drug-drug interactions cases, while valproic acid OR=221 (95% CI, 93.900 – 522.00) was associated with the highest odds of drug-drug interactions. Conclusion: Despite any data limitations, physicians prescribing risperidone in males should be aware of the high rates of adverse drug events and an alternative psychotropic should be considered in male patients. Further, patients with a history of pneumothorax or genetically predisposed to pneumothorax should be considered for an alternative antiemetic to ondansetron hydrochloride, due to increased odds associated with the drug and adverse event.

Abstract 2199: Establishment and high-throughput drug testing of multiple patient-derived cells from each renal cancer; intratumor heterogeneity of drug response and implications for precision medicine

Abstract Renal cell cancer (RCC) has become a prototype example of the extensive intra-tumor genetic heterogeneity and clonal evolution of human cancers. There is, however, little information on how the genetic heterogeneity will impact on drug responsiveness of the cancer cells. Establishment of multiple patient-derived cells (PDCs) from individual patients may help us to understand genomic clonal evolution pathways in cancer, explore intratumor heterogeneity of drug response across tumors, as well as help to define combinatorial treatments needed to target multiple subclones in a cancer patient. Here, we established several PDCs from different tumor regions of four RCC patients, verified clonal relationships of the PDCs with each others and with the uncultured tumor tissue by genome sequencing. PDCs retained cancer-specific copy number alterations and mutations in driver genes such as VHL, PBRM1, PIK3C2A, KMD5C and TSC2 genes, but also showed differences indicating genetic heterogeneity and clonal evolution, and thus generating a set of natural isogenic variants. Comprehensive drug-sensitivity testing of the PDC clones with 460 oncology drugs was performed. The drug testing analysis identified shared vulnerability of the PDCs towards several approved RCC drugs, such as mTOR-inhibitor (temsirolimus) and multi-kinase-inhibitor (pazopanib). The individual RCC PDC clones from different tumor regions in one patient showed distinct drug response profiles, confirming that genomic heterogeneity contributes to the variability in drug responses. We developed a capability to map drug response evolution on top of genomic evolution in cancer. In conclusion, our study suggested that comparison of drug response profiles among multiple (isogenic) PDC variants from a cancer patient may be informative for i) assessing intratumor heterogeneity in drug response ii) elucidating pharmacogenomic biomarkers among natural isogenic cell variants and iii) identifying drugs and drug combinations that may kill the multiple cancer subclones in a patient. Funding: EU-FP7-Systems Microscopy Network of Excellence, Sigrid Juselius Foundation, Cancer Society of Finland, Academy of Finland, the Magnus Ehrnrooth Foundation, TEKES FiDiPro Fellow Grant and Knut and Alice Wallenberg Foundation. Citation Format: Khalid Saeed, Poojitha Ojamies, Teijo Pellinen, Samuli Eldfors, Riku Turkki, Johan Lundin, Harry Nisen, Petrus Järvinen, Kimmo Taari, Taija af Hällström, Antti Rannikko, Tuomas Mirtti, Olli Kallioniemi, Päivi Östling. Establishment and high-throughput drug testing of multiple patient-derived cells from each renal cancer; intratumor heterogeneity of drug response and implications for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2199.

P-206 - Intensive first line FIr-C/FOx-C association of triplet chemotherapy plus cetuximab in RAS wild-type metastatic colorectal cancer patients: Preliminary phase II data and individual limiting toxicity syndromes prediction by pharmacogenomic biomarkers

P-206 - Intensive first line FIr-C/FOx-C association of triplet chemotherapy plus cetuximab in RAS wild-type metastatic colorectal cancer patients: Preliminary phase II data and individual limiting toxicity syndromes prediction by pharmacogenomic biomarkers

An opportunity for clinical pharmacology trained physicians to improve patient drug safety: A retrospective analysis of adverse drug reactions in teenagers

Background: Adverse drug reactions (ADRs) are a major cause of hospital admissions, prolonged hospital stays, morbidity, and drug-related mortality. In this study, we sought to identify the most frequently reported medications and associated side effects in adolescent-aged patients in an effort to prioritize clinical pharmacology consultation efforts for hospitals seeking to improve patient safety. Methods: Quarterly reported data were obtained from the United States Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2014 and ending in the third quarter of 2017. We then used the GeneCards database to map the pharmacogenomic biomarkers associated with the most reported FAERS drugs. Data homogenization and statistics analysis were all conducted in R for statistical programming. Results: We identified risperidone (10.64%) as the compound with the most reported ADRs from all reported cases. Males represented 90.1% of reported risperidone cases with gynecomastia being the most reported ADR. Ibuprofen OR=188 (95% CI, 105.0000 – 335.000) and quetiapine fumarate OR=116 (95% CI, 48.4000 – 278.000) were associated with the highest odds of completed suicide in teenagers. Ondansetron hydrochloride OR=7.12 (95% CI, 1.59 – 31.9) resulted in the highest odds of pneumothorax. Lastly, olanzapine (8.96%) represented the compound with the most reported drug-drug interactions cases, while valproic acid OR=221 (95% CI, 93.9000 – 522.000) was associated with the highest odds of drug-drug interactions. Conclusion: Despite any data limitations, physicians prescribing risperidone in males should be aware of the high rates of adverse drug events and an alternative psychotropic should be considered in male patients. Further, patients with a history of pneumothorax or genetically predisposed to pneumothorax should be considered for an alternative antiemetic to ondansetron hydrochloride, due to increased odds associated with the drug and adverse event.