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Multiple Loci Modulate Opioid Therapy Response for Cancer Pain

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Multiple Loci Modulate Opioid Therapy Response for Cancer Pain Author Manuscript Published OnlineFirst on May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Multiple loci modulate opioid therapy response for 2 cancer pain 3 4 Antonella Galvan 1, Frank Skorpen 2, Pål Klepstad 2,3, Anne Kari Knudsen 2, Torill 5 Fladvad 2, Felicia S. Falvella 1, Alessandra Pigni 1, Cinzia Brunelli 1, Augusto 6 Caraceni 1,2, Stein Kaasa 2,4, Tommaso A. Dragani 1 7 8 9 10 1 Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy; 2 European Palliative 11 Care Research Center, Medical Faculty, Norwegian University of Science and 12 Technology, Trondheim, Norway; 3 Department of Anaesthesiology and Emergency 13 Medicine and 4 Department of Oncology, St. Olavs University Hospital, Trondheim, 14 Norway 15 16 17 Conflict of interest statement 18 None declared. 19 20 Correspondence to: 21 Tommaso A. Dragani, Department of Predictive and Preventive Medicine, 22 Fondazione IRCCS Istituto Nazionale Tumori, Via G.A. Amadeo 42, 20133 23 Milan, Italy. Tel.: +39-0223902642, Fax: +39-0223902764. E-Mail: 24 [email protected] 25 Running Title: Genetic control of cancer pain response 26 Keywords: opioids, pharmacogenomics, pain, polygenic diseases, single-nucleotide 27 polymorphisms. 28 1 Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2011 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Abstract 2 Purpose: Patients treated with opioid drugs for cancer pain experience different 3 relief responses, raising the possibility that genetic factors play a role in opioid 4 therapy outcome. In this study, we tested the hypothesis that genetic variations may 5 control individual response to opioid drugs in cancer patients. 6 Experimental design: We tested one million SNPs in European cancer patients 7 selected in a first series for extremely poor (pain relief ≤40%; n=145) or good (pain 8 relief ≥90%; n=293) responses to opioid therapy using a DNA pooling approach. 9 Candidate SNPs identified by SNP-array were genotyped in individual samples 10 constituting DNA pools as well as in a second series of 570 patients. 11 Results: Association analysis in 1008 cancer patients identified 8 SNPs significantly 12 associated with pain relief at a statistical threshold of P<1.0 x 10-3, with rs12948783, 13 upstream of the RHBDF2 gene, showing the best statistical association (P=8.1 x 10- 14 9). Functional annotation analysis of SNP-tagged genes suggested the involvement 15 of genes acting on neurological system processes. 16 Conclusion: Our results indicate that the identified SNP panel can modulate the 17 response of cancer patients to opioid therapy and may provide a new tool for 18 personalized therapy of cancer pain. 19 2 Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2011 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Translational relevance 2 Pain is a common complication of almost all types of advanced cancer, and 3 treatment with opioids produces variable responses of pain relief among individuals. 4 Indeed, a variable percentage of cancer patients experiences reduced or poor 5 benefit from opioid therapy, with impairment of their quality of life. Our association 6 study in 1008 European cancer patients identified 8 SNPs statistically associated 7 with pain relief, which together might modulate the response of cancer patients to 8 opioid therapy and might serve as targetable tools to design new drugs. Our 9 functional annotation analysis of SNP-tagged genes revealed for the first time the 10 involvement of genes acting on neurological system processes rather than on opioid 11 metabolism or pharmacodynamics. Thus, the identified SNP panel may provide a 12 new tool for personalized therapy of cancer pain. 13 3 Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2011 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Introduction 2 Pain is the most dreaded symptom in patients with malignant disease. Indeed, 3 each year several million men and women of all ages suffer from some form of 4 invasive cancer associated with pain requiring chronic opioid treatment. At present, 5 opioids are virtually the only analgesics able to control moderate and severe cancer 6 pain. 7 While opioids are widely used to treat cancer pain, clinical studies indicate 8 great variability among individuals in the efficacy of these drugs and their side effects 9 (1, 2). Many studies have investigated the potential genetic basis of individual 10 variability in opioid drug response, but almost all have focused on a few candidate 11 genes involved in opioid pharmacokinetics or pharmacodynamics, and results are not 12 consistent among these often small studies (3, 4). For example, the catecholamines 13 dopamine, epinephrine and norepinephrine and their hydroxylated metabolites are 14 substrates for the enzyme catechol-O-methyltransferase encoded by the COMT 15 gene, in which genetic variations have been associated with variations in pain 16 perception and in doses of opioids required for pain treatment; however, 17 discrepancies in the results from different association studies on COMT 18 polymorphisms and pain perception have been reported, possibly due to the small 19 size of the studies (reviewed in (5)). 20 The human mu 1 opioid receptor (OPRM1) gene, which is the main 21 pharmacologic target of opioids, contains more than 100 polymorphisms, and these 22 polymorphisms might affect opioid binding affinity to the receptor and, consequently, 23 opioid-induced analgesia, tolerance, and dependence. However, a recent meta- 24 analysis casts doubt on the clinical relevance of the OPRM1 118A>G polymorphism 25 suggested by both preclinical and clinical studies as a candidate in personalized 26 cancer pain therapy (6). 27 A complex genetics underlying inter-individual differences in opioid response 28 is expected in light of the biochemical and biological complexity of the drug response 29 and of pain control. Indeed, opioid effects are governed by drug-related absorption, 30 distribution, metabolism, intrinsic efficacy of the receptors involved, and by signal 31 pathways downstream of the receptors. Since each of these steps in pain control is 32 modulated by several genes, each of which can exist in allelic forms in the general 4 Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2011 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 population, a genome-wide comprehensive analysis would be required to identify all 2 genetic factors involved. 3 Herein, we conducted the first genome-wide association study (GWAS) in 4 cancer patients treated with opioids using a 1-million single-nucleotide polymorphism 5 (SNP)-array to identify new genetic variations modulating individual pain relief. 6 Patients and methods 7 Study design 8 The European Pharmacogenetic Opioid Study (EPOS) represents a 9 multicenter effort to examine symptoms, pharmacology, and pharmacogenomics 10 related to the use of opioids. Recruitment criteria of patients included a regularly 11 scheduled opioid treatment stable for at least 3 days. Opioids administered to >5% of 12 patients included morphine, oxycodone, and fentanyl. The study includes 2294 13 cancer patients treated with opioids for moderate or severe pain and recruited from 14 17 centers of 11 European countries. The participation of the Research Steering 15 Committee from the European Association for Palliative Care (EAPC) facilitated 16 international cooperation in the study. 17 The study collected demographic and personal data, as well as malignant 18 diagnosis and opioid treatment from cancer patients. Opioid doses were converted to 19 equivalent total daily oral morphine doses (7). Pain was assessed by completing the 20 Brief Pain Inventory (BPI) (8). Functional status was measured by the Karnofsky 21 performance status score. Further details about the EPOS series, including opioid 22 doses and recruitments, have been reported (7). The study collected whole blood for 23 genetic analysis and has established a biobank. 24 Pain relief phenotype and DNA pool construction 25 The pain relief phenotype under study is semi-quantitative and determined 26 based on the BPI, a robust and psychometric validated method to assess the 27 subjective severity of pain. Pain relief is measured using an 11-point numerical rating 28 scale, from 0%, representing “no pain relief,” to 100% or “complete pain relief", i.e., 29 0%, 10%, 20%, etc. (9). 30 For DNA pool preparation, we first defined the cancer patients as “good” or 5 Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2011 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 “poor” responders to opioid therapy, based on their pain relief phenotype score 2 ≥90% or ≤40%, respectively (Fig. 1). Selection was stratified by gender and country 3 of origin, with a 1:2 matching of “poor” and “good” responders, to obtain a balanced 4 representation of patients in the two groups. DNA pools of “good” (n=293, mean pain 5 relief, 94.1%) and “poor” (n=145, mean pain relief, 27.0%) responders were created 6 using equal amounts of fluorimetrically measured DNA from each sample.
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