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2. Discovering novel germline genetic variants linked to severe fluoropyrimidine-related toxicity in- and outside DPYD

Author

Knikman, Jonathan E., Zhai, Qinglian, Lunenburg, Carin A. T. C., Henricks, Linda M., Böhringer, Stefan, van der Lee, Maaike, de Man, Femke M., Offer, Steven M., Shrestha, Shikshya, Creemers, Geert-Jan, Baars, Arnold, Dezentjé, Vincent O., Imholz, Alexander L. T., Jeurissen, Frank J. F., Portielje, Johanna E. A., Jansen, Rob L. H., Hamberg, Paul, Droogendijk, Helga J., Koopman, Miriam, Nieboer, Peter, van de Poel, Marlène H. W., Mandigers, Caroline M. P. W., van Schaik, Ron H. N., Gelderblom, Hans, Mathijssen, Ron H. J., Schellens, Jan H. M., Cats, Annemieke, Guchelaar, Henk-Jan, and Swen, Jesse J.

Published
2024
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4. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

Author

Wang, Anqi, Shen, Jiayi, Rodriguez, Alex A., Saunders, Edward J., Chen, Fei, Janivara, Rohini, Darst, Burcu F., Sheng, Xin, Xu, Yili, Chou, Alisha J., Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Plym, Anna, Sahimi, Ali, Hoffman, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figuerêdo, Jéssica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy P., Schleutker, Johanna, Tammela, Teuvo L. J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher T., Cho, Kelly, Mcmahon, Benjamin H., Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Borge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Røder, Andreas, Stroomberg, Hein V., Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith A., Tilly, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael B., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Jr., Ian M., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet L., Ostrander, Elaine A., Koutros, Stella, Beane Freeman, Laura E., Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan-Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M. L., Barnett, Gill, Maier, Christiane, Schnoeller, Thomas, Luedeke, Manuel, Kibel, Adam S., Drake, Bettina F., Cussenot, Olivier, Cancel-Tassin, Geraldine, Menegaux, Florence, Truong, Thérèse, Koudou, Yves Akoli, John, Esther M., Grindedal, Eli Marie, Maehle, Lovise, Khaw, Kay-Tee, Ingles, Sue A., Stern, Mariana C., Vega, Ana, Gómez-Caamaño, Antonio, Fachal, Laura, Rosenstein, Barry S., Kerns, Sarah L., Ostrer, Harry, Teixeira, Manuel R., Paulo, Paula, Brandão, Andreia, Watya, Stephen, Lubwama, Alexander, Bensen, Jeannette T., Butler, Ebonee N., Mohler, James L., Taylor, Jack A., Kogevinas, Manolis, Dierssen-Sotos, Trinidad, Castaño-Vinyals, Gemma, Cannon-Albright, Lisa, Teerlink, Craig C., Huff, Chad D., Pilie, Patrick, Yu, Yao, Bohlender, Ryan J., Gu, Jian, Strom, Sara S., Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Kaneva, Radka, Slavov, Chavdar, Mitev, Vanio, Leach, Robin J., Brenner, Hermann, Chen, Xuechen, Holleczek, Bernd, Schöttker, Ben, Klein, Eric A., Hsing, Ann W., Kittles, Rick A., Murphy, Adam B., Logothetis, Christopher J., Kim, Jeri, Neuhausen, Susan L., Steele, Linda, Ding, Yuan Chun, Isaacs, William B., Nemesure, Barbara, Hennis, Anselm J. M., Carpten, John, Pandha, Hardev, Michael, Agnieszka, De Ruyck, Kim, De Meerleer, Gert, Ost, Piet, Xu, Jianfeng, Razack, Azad, Lim, Jasmine, Teo, Soo-Hwang, Newcomb, Lisa F., Lin, Daniel W., Fowke, Jay H., Neslund-Dudas, Christine M., Rybicki, Benjamin A., Gamulin, Marija, Lessel, Davor, Kulis, Tomislav, Usmani, Nawaid, Abraham, Aswin, Singhal, Sandeep, Parliament, Matthew, Claessens, Frank, Joniau, Steven, Van den Broeck, Thomas, Gago-Dominguez, Manuela, Castelao, Jose Esteban, Martinez, Maria Elena, Larkin, Samantha, Townsend, Paul A., Aukim-Hastie, Claire, Bush, William S., Aldrich, Melinda C., Crawford, Dana C., Srivastava, Shiv, Cullen, Jennifer, Petrovics, Gyorgy, Casey, Graham, Wang, Ying, Tettey, Yao, Lachance, Joseph, Tang, Wei, Biritwum, Richard B., Adjei, Andrew A., Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Yamoah, Kosj, Govindasami, Koveela, Chokkalingam, Anand P., Keaton, Jacob M., Hellwege, Jacklyn N., Clark, Peter E., Jalloh, Mohamed, Gueye, Serigne M., Niang, Lamine, Ogunbiyi, Olufemi, Shittu, Olayiwola, Amodu, Olukemi, Adebiyi, Akindele O., Aisuodionoe-Shadrach, Oseremen I., Ajibola, Hafees O., Jamda, Mustapha A., Oluwole, Olabode P., Nwegbu, Maxwell, Adusei, Ben, Mante, Sunny, Darkwa-Abrahams, Afua, Diop, Halimatou, Gundell, Susan M., Roobol, Monique J., Jenster, Guido, van Schaik, Ron H. N., Hu, Jennifer J., Sanderson, Maureen, Kachuri, Linda, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Preuss, Michael H., Loos, Ruth J. F., Zawistowski, Matthew, Zöllner, Sebastian, Lu, Zeyun, Van Den Eeden, Stephen K., Easton, Douglas F., Ambs, Stefan, Edwards, Todd L., Mägi, Reedik, Rebbeck, Timothy R., Fritsche, Lars, Chanock, Stephen J., Berndt, Sonja I., Wiklund, Fredrik, Nakagawa, Hidewaki, Witte, John S., Gaziano, J. Michael, Justice, Amy C., Mancuso, Nick, Terao, Chikashi, Eeles, Rosalind A., Kote-Jarai, Zsofia, Madduri, Ravi K., Conti, David V., and Haiman, Christopher A.

Published
2023
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7. Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Author

Huynh-Le, Minh-Phuong, Karunamuni, Roshan, Fan, Chun Chieh, Asona, Lui, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth R, Lophatananon, Artitaya, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Blot, William J, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Wiklund, Fredrik, Penney, Kathryn L, Huff, Chad D, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Ost, Piet, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Abraham, Aswin, Claessens, Frank, Castelao, Jose Esteban, Townsend, Paul A, Crawford, Dana C, Petrovics, Gyorgy, van Schaik, Ron HN, Parent, Marie-Élise, Hu, Jennifer J, Zheng, Wei, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Urologic Diseases, Prevention, Genetics, Good Health and Well Being, Male, Humans, Prostate-Specific Antigen, Prostatic Neoplasms, Early Detection of Cancer, Polymorphism, Single Nucleotide, Risk Factors, Risk Assessment, Genetic Predisposition to Disease, UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionsWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Published
2022

8. DPYD Genotyping Recommendations: A Joint Consensus Recommendation of the Association for Molecular Pathology, American College of Medical Genetics and Genomics, Clinical Pharmacogenetics Implementation Consortium, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, European Society for Pharmacogenomics and Personalized Therapy, Pharmacogenomics Knowledgebase, and Pharmacogene Variation Consortium

Author

Pratt, Victoria M., Cavallari, Larisa H., Fulmer, Makenzie L., Gaedigk, Andrea, Hachad, Houda, Ji, Yuan, Kalman, Lisa V., Ly, Reynold C., Moyer, Ann M., Scott, Stuart A., Turner, Amy J., van Schaik, Ron H.N., Whirl-Carrillo, Michelle, and Weck, Karen E.

Published
2024
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10. CYP3A4*22 Genotype-Guided Dosing of Kinase Inhibitors in Cancer Patients

Author

van Eerden, Ruben A. G., IJzerman, Nikki S., van Meekeren, Milan, Oomen-de Hoop, Esther, Guchelaar, Niels A. D., Visser, Andrea M. W., Matic, Maja, van Schaik, Ron H. N., de Bruijn, Peter, Moes, Dirk-Jan A. R., Jobse, Pieter A., Gelderblom, Hans, Huitema, Alwin D. R., Steeghs, Neeltje, Mathijssen, Ron H. J., and Koolen, Stijn L. W.

Published
2023
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13. Principles of Pharmacogenetics

Author

van Schaik, Ron H. N., Bach-Rojecky, Lidija, Primorac, Dragan, Primorac, Dragan, editor, Höppner, Wolfgang, editor, and Bach-Rojecky, Lidija, editor

Published
2023
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18. Correction: Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene–drug interaction between UGT1A1 and irinotecan

Author

Hulshof, Emma C., Deenen, Maarten J., Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J., Buunk, Anne-Marie, Houwink, Elisa J. F., Risselada, Arne, Rongen, Gerard A. P. J. M., van Schaik, Ron H. N., Touw, Daan J., van der Weide, Jan, van Westrhenen, Roos, Deneer, Vera H. M., Guchelaar, Henk-Jan, and Swen, Jesse J.

Published
2023
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19. Influence of germline variations in drug transporters ABCB1 and ABCG2 on intracerebral osimertinib efficacy in patients with non-small cell lung cancer

Author

Veerman, G.D. Marijn, Boosman, Rene J., Jebbink, Merel, Oomen-de Hoop, Esther, van der Wekken, Anthonie J., Bahce, Idris, Hendriks, Lizza E.L., Croes, Sander, Steendam, Christi M.J., de Jonge, Evert, Koolen, Stijn L.W., Steeghs, Neeltje, van Schaik, Ron H.N., Smit, Egbert F., Dingemans, Anne-Marie C., Huitema, Alwin D.R., and Mathijssen, Ron H.J.

Published
2023
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29. Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC) for CYP2D6, ADRB1, ADRB2, ADRA2C, GRK4, and GRK5 Genotypes and Beta‐Blocker Therapy.

Author

Duarte, Julio D., Thomas, Cameron D., Lee, Craig R., Huddart, Rachel, Agundez, Jose A. G., Baye, Jordan F., Gaedigk, Andrea, Klein, Teri E., Lanfear, David E., Monte, Andrew A., Nagy, Mohamed, Schwab, Matthias, Stein, C. Michael, Uppugunduri, Chakradhara Rao S., van Schaik, Ron H. N., Donnelly, Roseann S., Caudle, Kelly E., and Luzum, Jasmine A.

Subjects
ARRHYTHMIA, CYTOCHROME P-450 CYP2D6, GENETIC variation, MYOCARDIAL infarction, HEART beat
Abstract

Beta‐blockers are widely used medications for a variety of indications, including heart failure, myocardial infarction, cardiac arrhythmias, and hypertension. Genetic variability in pharmacokinetic (e.g., CYP2D6) and pharmacodynamic (e.g., ADRB1, ADRB2, ADRA2C, GRK4, GRK5) genes have been studied in relation to beta‐blocker exposure and response. We searched and summarized the strength of the evidence linking beta‐blocker exposure and response with the six genes listed above. The level of evidence was high for associations between CYP2D6 genetic variation and both metoprolol exposure and heart rate response. Evidence indicates that CYP2D6 poor metabolizers experience clinically significant greater exposure and lower heart rate in response to metoprolol compared with those who are not poor metabolizers. Therefore, we provide therapeutic recommendations regarding genetically predicted CYP2D6 metabolizer status and metoprolol therapy. However, there was insufficient evidence to make therapeutic recommendations for CYP2D6 and other beta‐blockers or for any beta‐blocker and the other five genes evaluated (updates at www.cpicpgx.org). [ABSTRACT FROM AUTHOR]

Published
2024
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30. Nonlinear Mixed‐Effects Model of Z‐Endoxifen Concentrations in Tamoxifen‐Treated Patients from the CEPAM Cohort.

Author

Mc Laughlin, Anna M., Helland, Thomas, Klima, Fenja, Koolen, Stijn L.W., van Schaik, Ron H.N., Mathijssen, Ron H.J., Neven, Patrick, Swen, Jesse J., Guchelaar, Henk‐Jan, Dalenc, Florence, White‐Koning, Melanie, Michelet, Robin, Mikus, Gerd, Schroth, Werner, Mürdter, Thomas, Brauch, Hiltrud, Schwab, Matthias, Søiland, Håvard, Mellgren, Gunnar, and Thomas, Fabienne

Subjects
CYTOCHROME P-450 CYP2D6, BIOTRANSFORMATION (Metabolism), TAMOXIFEN, BREAST cancer, BODY weight
Abstract

Tamoxifen is widely used in patients with hormone receptor‐positive breast cancer. The polymorphic enzyme CYP2D6 is primarily responsible for metabolic activation of tamoxifen, resulting in substantial interindividual variability of plasma concentrations of its most important metabolite, Z‐endoxifen. The Z‐endoxifen concentration thresholds below which tamoxifen treatment is less efficacious have been proposed but not validated, and prospective trials of individualized tamoxifen treatment to achieve Z‐endoxifen concentration thresholds are considered infeasible. Therefore, we aim to validate the association between Z‐endoxifen concentration and tamoxifen treatment outcomes, and identify a Z‐endoxifen concentration threshold of tamoxifen efficacy, using pharmacometric modeling and simulation. As a first step, the CYP2D6 Endoxifen Percentage Activity Model (CEPAM) cohort was created by pooling data from 28 clinical studies (> 7,000 patients) with measured endoxifen plasma concentrations. After cleaning, data from 6,083 patients were used to develop a nonlinear mixed‐effect (NLME) model for tamoxifen and Z‐endoxifen pharmacokinetics that includes a conversion factor to allow inclusion of studies that measured total endoxifen but not Z‐endoxifen. The final parent‐metabolite NLME model confirmed the primary role of CYP2D6, and contributions from body weight, CYP2C9 phenotype, and co‐medication with CYP2D6 inhibitors, on Z‐endoxifen pharmacokinetics. Future work will use the model to simulate Z‐endoxifen concentrations in patients receiving single agent tamoxifen treatment within large prospective clinical trials with long‐term survival to identify the Z‐endoxifen concentration threshold below which tamoxifen is less efficacious. Identification of this concentration threshold would allow personalized tamoxifen treatment to improve outcomes in patients with hormone receptor‐positive breast cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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31. TGF-β mRNA levels in circulating extracellular vesicles are associated with response to anti-PD1 treatment in metastatic melanoma

Author

Crucitta, Stefania, primary, Cucchiara, Federico, additional, Marconcini, Riccardo, additional, Bulleri, Alessandra, additional, Manacorda, Simona, additional, Capuano, Annalisa, additional, Cioni, Dania, additional, Nuzzo, Amedeo, additional, de Jonge, Evert, additional, Mathjissen, Ron H. J., additional, Neri, Emanuele, additional, van Schaik, Ron H. N., additional, Fogli, Stefano, additional, Danesi, Romano, additional, and Del Re, Marzia, additional

Published
2024
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32. Clinical utility of circulating tumor DNA in patients with advanced KRASG12C-mutated non-small cell lung cancer treated with sotorasib.

Author

Ernst, Sophie M., primary, van Marion, Ronald, additional, Atmodimedjo, Peggy N., additional, de Jonge, Evert, additional, Mathijssen, Ron H.J., additional, Paats, Marthe S., additional, de Bruijn, Peter, additional, Koolen, Stijn L., additional, von der Thüsen, Jan H., additional, Aerts, Joachim G.J.V., additional, van Schaik, Ron H.N., additional, Dubbink, Hendrikus J., additional, and Dingemans, Anne-Marie C., additional

Published
2024
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34. External Quality Assessment on Molecular Tumor Profiling with Circulating Tumor DNA-Based Methodologies Routinely Used in Clinical Pathology within the COIN Consortium

Author

van der Leest, Paul, primary, Rozendal, Pim, additional, Hinrichs, John, additional, van Noesel, Carel J M, additional, Zwaenepoel, Karen, additional, Deiman, Birgit, additional, Huijsmans, Cornelis J J, additional, van Eijk, Ronald, additional, Speel, Ernst Jan M, additional, van Haastert, Rick J, additional, Ligtenberg, Marjolijn J L, additional, van Schaik, Ron H N, additional, Jansen, Maurice P H M, additional, Dubbink, Hendrikus J, additional, de Leng, Wendy W, additional, Leers, Mathie P G, additional, Tamminga, Menno, additional, van den Broek, Daan, additional, van Kempen, Léon C, additional, and Schuuring, Ed, additional

Published
2024
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35. Effects of CTLA-4 Single Nucleotide Polymorphisms on Toxicity of Ipilimumab-Containing Regimens in Patients With Advanced Stage Melanoma

Author

de Joode, Karlijn, primary, Mora, Alfonso Rojas, additional, van Schaik, Ron H. N., additional, Zippelius, Alfred, additional, van der Veldt, Astrid, additional, Gerard, Camille Léa, additional, Läubli, Heinz, additional, Michielin, Olivier, additional, von Moos, Roger, additional, Joerger, Markus, additional, Levesque, Mitchell P., additional, Aeppli, Stefanie, additional, Mangana, Johanna, additional, Mangas, Cristina, additional, Trost, Nadine, additional, Meyer, Stefan, additional, Parvex, Sandra Leoni, additional, Mathijssen, Ron, additional, and Metaxas, Yannis, additional

Published
2024
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36. DPYDGenotyping Recommendations

Author

Pratt, Victoria M., Cavallari, Larisa H., Fulmer, Makenzie L., Gaedigk, Andrea, Hachad, Houda, Ji, Yuan, Kalman, Lisa V., Ly, Reynold C., Moyer, Ann M., Scott, Stuart A., Turner, Amy J., van Schaik, Ron H.N., Whirl-Carrillo, Michelle, and Weck, Karen E.

Abstract

The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations for a minimum set of variant alleles (tier 1) and an extended list of variant alleles (tier 2) that will aid clinical laboratories when designing assays for PGx testing. The Association for Molecular Pathology PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, and other technical considerations for PGx testing when developing these recommendations. The goal of this Working Group is to promote standardization of PGx testing across clinical laboratories. This document will focus on clinical DPYDPGx testing that may be applied to all dihydropyrimidine dehydrogenase–related medications. These recommendations are not to be interpreted as prescriptive but to provide a reference guide.

Published
2024
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37. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction of CYP2C9, HLA-Aand HLA-Bwith anti-epileptic drugs

Author

Manson, Lisanne E. N., Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J., Buunk, Anne-Marie, Houwink, Elisa J. F., Risselada, Arne, Rongen, Gerard A. P. J. M., van Schaik, Ron H. N., Swen, Jesse J., Touw, Daan J., van Westrhenen, Roos, Deneer, Vera H. M., and Guchelaar, Henk-Jan

Abstract

By developing evidence-based pharmacogenetics guidelines to optimize pharmacotherapy, the Dutch Pharmacogenetics Working Group (DPWG) aims to advance the implementation of pharmacogenetics (PGx). This guideline outlines the gene-drug interaction of CYP2C9and HLA-Bwith phenytoin, HLA-Aand HLA-Bwith carbamazepine and HLA-Bwith oxcarbazepine and lamotrigine. A systematic review was performed and pharmacotherapeutic recommendations were developed. For CYP2C9intermediate and poor metabolisers, the DPWG recommends lowering the daily dose of phenytoin and adjust based on effect and serum concentration after 7–10 days. For HLA-B*15:02carriers, the risk of severe cutaneous adverse events associated with phenytoin, carbamazepine, oxcarbazepine, and lamotrigine is strongly increased. For carbamazepine, this risk is also increased in HLA-B*15:11and HLA-A*31:01carriers. For HLA-B*15:02, HLA-B*15:11and HLA-A*31:01positive patients, the DPWG recommends choosing an alternative anti-epileptic drug. If not possible, it is recommended to advise the patient to report any rash while using carbamazepine, lamotrigine, oxcarbazepine or phenytoin immediately. Carbamazepine should not be used in an HLA-B*15:02positive patient. DPWG considers CYP2C9genotyping before the start of phenytoin “essential” for toxicity prevention. For patients with an ancestry in which the abovementioned HLA-alleles are prevalent, the DPWG considers HLA-B*15:02genotyping before the start of carbamazepine, phenytoin, oxcarbazepine, and lamotrigine “beneficial”, as well as genotyping for HLA-B*15:11and HLA-A*31:01before initiating carbamazepine.

Published
2024
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39. Dutch pharmacogenetics working group guideline for the gene-drug interaction of ABCG2, HLA-B and Allopurinol, and MTHFR, folic acid and methotrexate

Author

Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, van der Pol, Karel H, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne Marie, Guchelaar, Henk-Jan, Risselada, Arne, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van der Weide, Jan, van Westrhenen, Roos, Deneer, Vera H M, Houwink, Elisa J F, Rongen, Gerard A, Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, van der Pol, Karel H, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne Marie, Guchelaar, Henk-Jan, Risselada, Arne, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van der Weide, Jan, van Westrhenen, Roos, Deneer, Vera H M, Houwink, Elisa J F, and Rongen, Gerard A

Published
2024

40. Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk

Author

de Bakker, Marie, Scholte, Niels T.B., Oemrawsingh, Rohit M., Umans, Victor A., Kietselaer, Bas, Schotborgh, Carl, Ronner, Eelko, Lenderink, Timo, Aksoy, Ismail, van der Harst, Pim, Asselbergs, Folkert W., Maas, Arthur, Oude Ophuis, Anton J., Krenning, Boudewijn, de Winter, Robbert J., The, S. Hong Kie, Wardeh, Alexander J., Hermans, Walter, Cramer, G. Etienne, van Schaik, Ron H., de Rijke, Yolanda B., Akkerhuis, K. Martijn, Kardys, Isabella, Boersma, Eric, de Bakker, Marie, Scholte, Niels T.B., Oemrawsingh, Rohit M., Umans, Victor A., Kietselaer, Bas, Schotborgh, Carl, Ronner, Eelko, Lenderink, Timo, Aksoy, Ismail, van der Harst, Pim, Asselbergs, Folkert W., Maas, Arthur, Oude Ophuis, Anton J., Krenning, Boudewijn, de Winter, Robbert J., The, S. Hong Kie, Wardeh, Alexander J., Hermans, Walter, Cramer, G. Etienne, van Schaik, Ron H., de Rijke, Yolanda B., Akkerhuis, K. Martijn, Kardys, Isabella, and Boersma, Eric

Abstract

BACKGROUND: We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk. METHODS AND RESULTS: BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS. CONCLUSIONS: Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they expe

Published
2024

42. Determination of cortisone and cortisol in human scalp hair using an improved LC-MS/MS-based method

Author

Mirzaian, Mina, Van Zundert, Sofie K.M., Schilleman, Wim F., Mohseni, Mostafa, Kuckuck, Susanne, Van Rossum, Elisabeth F.C., Van Schaik, Ron H.N., Van Den Berg, Sjoerd A.A., Mirzaian, Mina, Van Zundert, Sofie K.M., Schilleman, Wim F., Mohseni, Mostafa, Kuckuck, Susanne, Van Rossum, Elisabeth F.C., Van Schaik, Ron H.N., and Van Den Berg, Sjoerd A.A.

Abstract

Objectives: Human scalp hair is an easily available but complex matrix for determination of cortisone and cortisol, and has been shown to reflect long-term glucocorticoid exposure. Hair glucocorticoid analysis has been used to detect hypo- and hypercortisolism. In this study, we describe the development and validation of a LC-MS/MS method for quantification of cortisone and cortisol in human scalp hair, and provide a novel approach for analysis and interpretation of the results.Methods:Improved sample preparation using pulverization and solid phase extraction allowed for low sample volumes (10 mg). Baseline chromatographic separation without matrix interference was achieved by reversed phase chromatography and MRM measurement in negative ion mode. Run-to-run time was 8 min. Mixed model analyses were performed to create individual patterns of cortisone and cortisol concentrations.Results:Matrix matched calibration curves showed excellent linearity up to 100 pg (analyte)/mg (hair) for both cortisone and cortisol (R2>0.995). LLOQ was 1.5 and 1.0 pg/mg for cortisone and cortisol, respectively. Matrix effect was negligible for hair color (recoveries 95-105 %). Cortisone and cortisol concentrations decreased from proximal to distal hair segments, following a predictable, but subject-specific pattern, with less individual variation for cortisone than for cortisol. Conclusions:This improved LC-MS/MS method is able to accurately quantify cortisone and cortisol in human hair with minimum matrix interference. This new way of data analysis and interpretation including individual patterns of cortisone and cortisol will be of help with detection of pathological concentrations in both the high - and the low ranges of glucocorticoids.

Published
2024

43. Modelling changes in the pharmacokinetics of tacrolimus during pregnancy after kidney transplantation:A retrospective cohort study

Author

Schagen, Maaike R., Ulu, Asiye Nur, Francke, Marith I., van de Wetering, Jacqueline, van Buren, Marleen C., Schoenmakers, Sam, Matic, Maja, van Schaik, Ron H.N., Hesselink, Dennis A., de Winter, Brenda C.M., Schagen, Maaike R., Ulu, Asiye Nur, Francke, Marith I., van de Wetering, Jacqueline, van Buren, Marleen C., Schoenmakers, Sam, Matic, Maja, van Schaik, Ron H.N., Hesselink, Dennis A., and de Winter, Brenda C.M.

Abstract

Aims: Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model. Methods: Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis. Results: A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7 L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P ≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance.Conclusions: Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is

Published
2024

44. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2D6, CYP3A4 and CYP1A2 and antipsychotics

Author

Apotheek Klinische Farmacie, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, van der Weide, Jan, Apotheek Klinische Farmacie, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, and van der Weide, Jan

Published
2024

45. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2D6, CYP3A4 and CYP1A2 and antipsychotics

Author

Afd Pharmacoepi & Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, Pharmacoepidemiology and Clinical Pharmacology, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, van der Weide, Jan, Afd Pharmacoepi & Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, Pharmacoepidemiology and Clinical Pharmacology, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, and van der Weide, Jan

Published
2024

46. External Quality Assessment on Molecular Tumor Profiling with Circulating Tumor DNA-Based Methodologies Routinely Used in Clinical Pathology within the COIN Consortium

Author

van der Leest, Paul, Rozendal, Pim, Hinrichs, John, van Noesel, Carel J.M., Zwaenepoel, Karen, Deiman, Birgit, Huijsmans, Cornelis J.J., van Eijk, Ronald, Speel, Ernst Jan M., van Haastert, Rick J., Ligtenberg, Marjolijn J.L., van Schaik, Ron H.N., Jansen, Maurice P.H.M., Dubbink, Hendrikus J., de Leng, Wendy W., Leers, Mathie P.G., Tamminga, Menno, van den Broek, Daan, van Kempen, Leon C., Schuuring, Ed, van der Leest, Paul, Rozendal, Pim, Hinrichs, John, van Noesel, Carel J.M., Zwaenepoel, Karen, Deiman, Birgit, Huijsmans, Cornelis J.J., van Eijk, Ronald, Speel, Ernst Jan M., van Haastert, Rick J., Ligtenberg, Marjolijn J.L., van Schaik, Ron H.N., Jansen, Maurice P.H.M., Dubbink, Hendrikus J., de Leng, Wendy W., Leers, Mathie P.G., Tamminga, Menno, van den Broek, Daan, van Kempen, Leon C., and Schuuring, Ed

Abstract

Background: Identification of tumor-derived variants in circulating tumor DNA (ctDNA) has potential as a sensitive and reliable surrogate for tumor tissue-based routine diagnostic testing. However, variations in pre(analytical) procedures affect the efficiency of ctDNA recovery. Here, an external quality assessment (EQA) was performed to determine the performance of ctDNA mutation detection work flows that are used in current diagnostic settings across laboratories within the Dutch COIN consortium (ctDNA on the road to implementation in The Netherlands). Methods: Aliquots of 3 high-volume diagnostic leukapheresis (DLA) plasma samples and 3 artificial reference plasma samples with predetermined mutations were distributed among 16 Dutch laboratories. Participating laboratories were requested to perform ctDNA analysis for BRAF exon 15, EGFR exon 18-21, and KRAS exon 2-3 using their regular circulating cell-free DNA (ccfDNA) analysis work flow. Laboratories were assessed based on adherence to the study protocol, overall detection rate, and overall genotyping performance. Results: A broad range of preanalytical conditions (e.g., plasma volume, elution volume, and extraction methods) and analytical methodologies (e.g., droplet digital PCR [ddPCR], small-panel PCR assays, and next-generation sequencing [NGS]) were used. Six laboratories (38%) had a performance score of >0.90; all other laboratories scored between 0.26 and 0.80. Although 13 laboratories (81%) reached a 100% overall detection rate, the therapeutically relevant EGFR p.(S752_I759del) (69%), EGFR p.(N771_H773dup) (50%), and KRAS p.(G12C) (48%) mutations were frequently not genotyped accurately. Conclusions: Divergent (pre)analytical protocols could lead to discrepant clinical outcomes when using the same plasma samples. Standardization of (pre)analytical work flows can facilitate the implementation of reproducible liquid biopsy testing in the clinical routine.

Published
2024

48. Model‐Based Prediction of Irinotecan‐Induced Grade 4 Neutropenia in Cancer Patients: Influence of Incorporating Germline Genetic Factors in the Model.

Author

Karas, Spinel, Mathijssen, Ron H. J., van Schaik, Ron H. N., Forrest, Alan, Wiltshire, Tim, Bies, Robert R., and Innocenti, Federico

Subjects
NEUTROPENIA, GENETIC models, RECEIVER operating characteristic curves, PROGRESSION-free survival, GERM cells, CANCER patients
Abstract

Neutropenia is the major dose‐limiting toxicity of irinotecan‐based therapy. The objective of this study was to assess whether inclusion of germline genetic variants into a population pharmacokinetic/pharmacodynamic model can improve prediction of irinotecan‐induced grade 4 neutropenia and identify novel variants of clinical value. A semimechanistic population pharmacokinetic/pharmacodynamic model was used to predict neutrophil response over time in 197 patients receiving irinotecan. Covariate analysis was performed for demographic/clinical factors and 4,781 genetic variants in 84 drug response‐ and toxicity‐related genes to identify covariates associated with neutrophil response. We evaluated the predictive value of the model for grade 4 neutropenia reflecting different clinical scenarios of available data on identified demographic/clinical covariates, baseline and post‐treatment absolute neutrophil counts (ANCs), individual pharmacokinetics, and germline genetic variation. Adding 8 genetic identified covariates (rs10929302 (UGT1A1), rs1042482 (DPYD), rs2859101 (HLA‐DQB3), rs61754806 (NR3C1), rs9266271 (HLA‐B), rs7294 (VKORC1), rs1051713 (ALOX5), and ABCB1 rare variant burden) to a model using only baseline ANCs improved prediction of irinotecan‐induced grade 4 neutropenia from area under the receiver operating characteristic curve (AUC‐ROC) of 50–64% (95% confidence interval (CI), 54–74%). Individual pharmacokinetics further improved the prediction to 74% (95% CI, 64–84%). When weekly ANC was available, the identified covariates and individual pharmacokinetics yielded no additional contribution to the prediction. The model including only ANCs at baseline and at week 1 achieved an AUC‐ROC of 78% (95% CI, 69–88%). Germline DNA genetic variants may contribute to the prediction of irinotecan‐induced grade 4 neutropenia when incorporated into a population pharmacokinetic/pharmacodynamic model. This approach is generalizable to drugs that induce neutropenia and ultimately allows for personalized intervention to enhance patient safety. [ABSTRACT FROM AUTHOR]

Published
2024
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50. A low aldosterone/renin ratio and high soluble ACE2 associate with COVID-19 severity

Author

Akin, Sakir, Schriek, Paula, van Nieuwkoop, Cees, Neuman, Rugina I., Meynaar, Iwan, van Helden, Erik J., Bouazzaoui, Hassan El, Baak, Remon, Veuger, Marjan, Mairuhu, Ronne A.T.A., van den Berg, Lettie, van Driel, Vincent, Visser, Loes E., de Jonge, Evert, Garrelds, Ingrid M., Duynstee, Johannes F.A.B., van Rooden, Jan Kees, Ludikhuize, Jeroen, Verdonk, Koen, Caliskan, Kadir, Jansen, Tim, van Schaik, Ron H.N., and Danser, A.H. Jan

Published
2021
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