32 results on '"Melloni, Giorgio E M"'
Search Results
2. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
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Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio E. M., Kanoni, Stavroula, Rayner, Nigel W., Bocher, Ozvan, Arruda, Ana Luiza, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Broadaway, K. Alaine, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Hakaste, Liisa, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kamanu, Frederick K., Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Melander, Olle, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Moura, Filipe A., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Ahlqvist, Emma, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Marston, Nicholas A., Ruff, Christian T., van Heel, David A., Finer, Sarah, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria
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- 2024
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3. Estimating and Presenting Hazard Ratios and Absolute Risks from a Cox Model with Complex Non-linear Interactions
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Bellavia, Andrea, primary, Melloni, Giorgio E M, additional, Park, Jeong-Gun, additional, Discacciati, Andrea, additional, and Murphy, Sabina A, additional
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- 2024
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4. Computational analysis of cancer genome sequencing data
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Cortés-Ciriano, Isidro, Gulhan, Doga C., Lee, Jake June-Koo, Melloni, Giorgio E. M., and Park, Peter J.
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- 2022
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5. Estimating and presenting hazard ratios and absolute risks from a Cox model with complex nonlinear interactions.
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Bellavia, Andrea, Melloni, Giorgio E M, Park, Jeong-Gun, Discacciati, Andrea, and Murphy, Sabina A
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STATISTICAL models , *MATHEMATICAL variables , *RISK assessment , *DATA analysis , *RESEARCH , *SURVIVAL analysis (Biometry) , *CONFIDENCE intervals , *PROPORTIONAL hazards models , *REGRESSION analysis - Abstract
Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Application of the Win Ratio Method in the ENGAGE AF-TIMI 48 Trial Comparing Edoxaban With Warfarin in Patients With Atrial Fibrillation.
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Bergmark, Brian A., Park, Jeong-Gun, Hamershock, Rose A., Melloni, Giorgio E. M., De Caterina, Raffaele, Antman, Elliott M., Ruff, Christian T., Rutman, Howard, Mercuri, Michele F., Lanz, Hans-Joachim, Braunwald, Eugene, and Giugliano, Robert P.
- Abstract
BACKGROUND: Cardiovascular trials often use a composite end point and a time-to-first event model. We sought to compare edoxaban versus warfarin using the win ratio, which offers data complementary to time-to-first event analysis, emphasizing the most severe clinical events. METHODS: ENGAGE AF-TIMI 48 (Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48) was a double-blind, randomized trial in which patients with atrial fibrillation were assigned 1:1:1 to a higher dose edoxaban regimen (60/30 mg daily), a lower dose edoxaban regimen (30/15 mg daily), or warfarin. In an exploratory analysis, we analyzed the trial outcomes using an unmatched win ratio approach. The win ratio for each edoxaban regimen was the total number of edoxaban wins divided by the number of warfarin wins for the following ranked clinical outcomes: 1: death; 2: hemorrhagic stroke; 3: ischemic stroke/systemic embolic event/epidural or subdural bleeding; 4: noncerebral International Society on Thrombosis and Haemostasis major bleeding; and 5: cardiovascular hospitalization. RESULTS: 21 105 patients were randomized to higher dose edoxaban regimen (N=7035), lower dose edoxaban regimen (N=7034), or warfarin (N=7046), yielding >49 million pairs for each treatment comparison. The median age was 72 years, 38% were women, and 59% had prior vitamin K antagonist use. The win ratio was 1.11 (95% CI, 1.05-1.18) for higher dose edoxaban regimen versus warfarin and 1.11 (95% CI, 1.05-1.18) for lower dose edoxaban regimen versus warfarin. The favorable impacts of edoxaban on death (34% of wins) and cardiovascular hospitalization (41% of wins) were the major contributors to the win ratio. Results consistently favored edoxaban in subgroups based on creatine clearance and dose reduction at baseline, with heightened benefit among those without prior vitamin K antagonist use. CONCLUSIONS: In a win ratio analysis of the ENGAGE AF-TIMI 48 trial, both dose regimens of edoxaban were superior to warfarin for the net clinical outcome incorporating ischemic and bleeding events. As the win ratio emphasizes the most severe clinical events, this analysis supports the superiority of edoxaban over warfarin in patients with atrial fibrillation. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Novel Polygenic Risk Score and Established Clinical Risk Factors for Risk Estimation of Aortic Stenosis
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Small, Aeron M., primary, Melloni, Giorgio E. M., additional, Kamanu, Frederick K., additional, Bergmark, Brian A., additional, Bonaca, Marc P., additional, O’Donoghue, Michelle L., additional, Giugliano, Robert P., additional, Scirica, Benjamin M., additional, Bhatt, Deepak, additional, Antman, Elliott M., additional, Raz, Itamar, additional, Wiviott, Stephen D., additional, Truong, Buu, additional, Wilson, Peter W. F., additional, Cho, Kelly, additional, O’Donnell, Christopher J., additional, Braunwald, Eugene, additional, Lubitz, Steve A., additional, Ellinor, Patrick, additional, Peloso, Gina M., additional, Ruff, Christian T., additional, Sabatine, Marc S., additional, Natarajan, Pradeep, additional, and Marston, Nicholas A., additional
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- 2024
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8. The origins and genetic interactions of KRAS mutations are allele- and tissue-specific
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Cook, Joshua H., Melloni, Giorgio E. M., Gulhan, Doga C., Park, Peter J., and Haigis, Kevin M.
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- 2021
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9. The Effect of PCSK9 Inhibition on the Risk of Venous Thromboembolism
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Marston, Nicholas A., Gurmu, Yared, Melloni, Giorgio E. M., Bonaca, Marc, Gencer, Baris, Sever, Peter S., Pedersen, Terje R., Keech, Anthony C., Roselli, Carolina, Lubitz, Steven A., Ellinor, Patrick T., O’Donoghue, Michelle L., Giugliano, Robert P., Ruff, Christian T., and Sabatine, Marc S.
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- 2020
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10. Detecting the mutational signature of homologous recombination deficiency in clinical samples
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Gulhan, Doga C., Lee, Jake June-Koo, Melloni, Giorgio E. M., Cortés-Ciriano, Isidro, and Park, Peter J.
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- 2019
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11. Clonal hematopoiesis, cardiovascular events and treatment benefit in 63,700 individuals from five TIMI randomized trials
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Marston, Nicholas A., Pirruccello, James P., Melloni, Giorgio E. M., Kamanu, Frederick, Bonaca, Marc P., Giugliano, Robert P., Scirica, Benjamin M., Wiviott, Stephen D., Bhatt, Deepak L., Steg, Philippe Gabriel, Raz, Itamar, Braunwald, Eugene, Libby, Peter, Ellinor, Patrick T., Bick, Alexander G., Sabatine, Marc S., and Ruff, Christian T.
- Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with an increased risk of cardiovascular (CV) disease in the general population. Currently, it is unclear whether this association is observed in large clinical trial cohorts with a high burden of existing CV disease or whether CV therapies can mitigate CHIP-associated CV risk. To address these questions, we studied 63,700 patients from five randomized trials that tested established therapies for CV disease, including treatments targeting the proteins PCSK9, SGLT2, P2Y12 and FXa. During a median follow-up of 2.5 years, 7,453 patients had at least one CV event (CV death, myocardial infarction (MI), ischemic stroke or coronary revascularization). The adjusted hazard ratio (aHR) for CV events for CHIP+ patients was 1.07 (95% CI: 0.99–1.16, P= 0.08), with consistent risk estimates across each component of CV risk. Significant heterogeneity in the risk of MI was observed, such that CHIP+ patients had a 30% increased risk of first MI (aHR = 1.31 (1.05–1.64), P= 0.02) but no increased risk of recurrent MI (aHR = 0.94 (0.79–1.13), Pint= 0.008), as compared to CHIP− patients. Moreover, no significant heterogeneity in treatment effect between individuals with and without CHIP was observed for any of the therapies studied in the five trials. These results indicate that in clinical trial populations, CHIP is associated with incident but not recurrent coronary events and that the presence of CHIP does not appear to identify patients who will derive greater benefit from commonly used CV therapies.
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- 2024
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12. Predictive Utility of a Coronary Artery Disease Polygenic Risk Score in Primary Prevention
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Marston, Nicholas A., primary, Pirruccello, James P., additional, Melloni, Giorgio E. M., additional, Koyama, Satoshi, additional, Kamanu, Frederick K., additional, Weng, Lu-Chen, additional, Roselli, Carolina, additional, Kamatani, Yoichiro, additional, Komuro, Issei, additional, Aragam, Krishna G., additional, Butterworth, Adam S., additional, Ito, Kaoru, additional, Lubitz, Steve A., additional, Ellinor, Patrick T., additional, Sabatine, Marc S., additional, and Ruff, Christian T., additional
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- 2022
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13. Predictive Utility of a Coronary Artery Disease Polygenic Risk Score in Primary Prevention.
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Marston, Nicholas A., Pirruccello, James P., Melloni, Giorgio E. M., Koyama, Satoshi, Kamanu, Frederick K., Weng, Lu-Chen, Roselli, Carolina, Kamatani, Yoichiro, Komuro, Issei, Aragam, Krishna G., Butterworth, Adam S., Ito, Kaoru, Lubitz, Steve A., Ellinor, Patrick T., Sabatine, Marc S., and Ruff, Christian T.
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- 2023
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14. Association of Apolipoprotein B–Containing Lipoproteins and Risk of Myocardial Infarction in Individuals With and Without Atherosclerosis
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Marston, Nicholas A., primary, Giugliano, Robert P., additional, Melloni, Giorgio E. M., additional, Park, Jeong-Gun, additional, Morrill, Valerie, additional, Blazing, Michael A., additional, Ference, Brian, additional, Stein, Evan, additional, Stroes, Erik S., additional, Braunwald, Eugene, additional, Ellinor, Patrick T., additional, Lubitz, Steven A., additional, Ruff, Christian T., additional, and Sabatine, Marc S., additional
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- 2022
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15. Blood gas analyses in hyperbaric and underwater environments: a systematic review
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Paganini, Matteo, primary, Moon, Richard E., additional, Boccalon, Nicole, additional, Melloni, Giorgio E. M., additional, Giacon, Tommaso A., additional, Camporesi, Enrico M., additional, and Bosco, Gerardo, additional
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- 2022
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16. Computational analysis of cancer genome sequencing data
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Cortés-Ciriano, Isidro, primary, Gulhan, Doga C., additional, Lee, Jake June-Koo, additional, Melloni, Giorgio E. M., additional, and Park, Peter J., additional
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- 2021
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17. Organizational aspects of pediatric anesthesia and surgery between two waves of Covid‐19
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Camporesi, Anna, primary, Melloni, Giorgio E. M., additional, Diotto, Veronica, additional, Bertani, Patrizia, additional, La Pergola, Enrico, additional, and Pelizzo, Gloria, additional
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- 2021
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18. Very low-depth whole-genome sequencing in complex trait association studies
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Gilly, Arthur, primary, Southam, Lorraine, additional, Suveges, Daniel, additional, Kuchenbaecker, Karoline, additional, Moore, Rachel, additional, Melloni, Giorgio E M, additional, Hatzikotoulas, Konstantinos, additional, Farmaki, Aliki-Eleni, additional, Ritchie, Graham, additional, Schwartzentruber, Jeremy, additional, Danecek, Petr, additional, Kilian, Britt, additional, Pollard, Martin O, additional, Ge, Xiangyu, additional, Tsafantakis, Emmanouil, additional, Dedoussis, George, additional, and Zeggini, Eleftheria, additional
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- 2018
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19. Mutations targeting the coagulation pathway are enriched in brain metastases
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Richichi, Cristina, primary, Fornasari, Lorenzo, additional, Melloni, Giorgio E. M., additional, Brescia, Paola, additional, Patanè, Monica, additional, Del Bene, Massimiliano, additional, Mustafa, Dana A. M., additional, Kros, Johan M., additional, Pollo, Bianca, additional, Pruneri, Giancarlo, additional, Sciandivasci, Angela, additional, Munzone, Elisabetta, additional, DiMeco, Francesco, additional, Pelicci, Pier Giuseppe, additional, Riva, Laura, additional, and Pelicci, Giuliana, additional
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- 2017
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20. A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data
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Melloni, Giorgio E. M., primary, Mazzarella, Luca, additional, Bernard, Loris, additional, Bodini, Margherita, additional, Russo, Anna, additional, Luzi, Lucilla, additional, Pelicci, Pier Giuseppe, additional, and Riva, Laura, additional
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- 2017
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21. Very low-depth whole-genome sequencing in complex trait association studies.
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Gilly, Arthur, Southam, Lorraine, Suveges, Daniel, Kuchenbaecker, Karoline, Moore, Rachel, Melloni, Giorgio E M, Hatzikotoulas, Konstantinos, Farmaki, Aliki-Eleni, Ritchie, Graham, Schwartzentruber, Jeremy, Danecek, Petr, Kilian, Britt, Pollard, Martin O, Ge, Xiangyu, Tsafantakis, Emmanouil, Dedoussis, George, and Zeggini, Eleftheria
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INTERNET servers ,GENOTYPES ,ALLELES ,BIOINFORMATICS ,PIPELINES ,GENOME-wide association studies ,EXOMES ,GENETIC correlations - Abstract
Motivation Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. Results We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. Availability and implementation The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter , the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
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- 2019
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22. LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer
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Melloni, Giorgio E. M., primary, de Pretis, Stefano, additional, Riva, Laura, additional, Pelizzola, Mattia, additional, Céol, Arnaud, additional, Costanza, Jole, additional, Müller, Heiko, additional, and Zammataro, Luca, additional
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- 2016
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23. INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments
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de Pretis, Stefano, primary, Kress, Theresia, additional, Morelli, Marco J., additional, Melloni, Giorgio E. M., additional, Riva, Laura, additional, Amati, Bruno, additional, and Pelizzola, Mattia, additional
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- 2015
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24. The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations
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Bodini, Margherita, primary, Ronchini, Chiara, additional, Giacò, Luciano, additional, Russo, Anna, additional, Melloni, Giorgio E. M., additional, Luzi, Lucilla, additional, Sardella, Domenico, additional, Volorio, Sara, additional, Hasan, Syed K., additional, Ottone, Tiziana, additional, Lavorgna, Serena, additional, Lo-Coco, Francesco, additional, Candoni, Anna, additional, Fanin, Renato, additional, Toffoletti, Eleonora, additional, Iacobucci, Ilaria, additional, Martinelli, Giovanni, additional, Cignetti, Alessandro, additional, Tarella, Corrado, additional, Bernard, Loris, additional, Pelicci, Pier Giuseppe, additional, and Riva, Laura, additional
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- 2015
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25. DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes.
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Melloni, Giorgio E. M., Ogier, Alessandro G. E., de Pretis, Stefano, Mazzarella, Luca, Pelizzola, Mattia, Pelicci, Pier Giuseppe, and Riva, Laura
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CANCER genetics , *GENETIC mutation , *GENOMES , *COHORT analysis , *CLINICAL trials ,TUMOR genetics - Abstract
A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available at https://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/. [ABSTRACT FROM AUTHOR]
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- 2014
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26. Risk of new‐onset diabetes and efficacy of pharmacological weight loss therapy.
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Moura, Filipe A., Bellavia, Andrea, Berg, David D., Melloni, Giorgio E. M., Feinberg, Mark W., Leiter, Lawrence A., Bohula, Erin A., Morrow, David A., Scirica, Benjamin A., Wiviott, Stephen D., and Sabatine, Marc S.
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WEIGHT loss , *TYPE 2 diabetes , *INDIVIDUALIZED medicine , *ANTIOBESITY agents , *DIABETES - Abstract
Aims Materials and Methods Results Conclusions To develop a clinical risk model to identify individuals at higher risk of developing new‐onset diabetes and who might benefit more from weight loss pharmacotherapy.A total of 21 143 patients without type 2 diabetes at baseline from two TIMI clinical trials of stable cardiovascular patients were divided into a derivation (~2/3) and validation (~1/3) cohort. The primary outcome was new‐onset diabetes. Twenty‐seven candidate risk variables were considered, and variable selection was performed using multivariable Cox regression. The final model was evaluated for discrimination and calibration, and for its ability to identify patients who experienced a larger benefit from the weight loss medication lorcaserin in terms of risk of new‐onset diabetes.During a median (interquartile range) follow‐up of 2.3 (1.8–2.7) years, new‐onset diabetes occurred in 1013 patients (7.7%). The final model included five independent predictors (glycated haemoglobin, fasting glucose, age, body mass index, and triglycerides/high‐density lipoprotein). The clinical risk model showed good discrimination (Harrell's C‐indices 0.802, 95% confidence interval [CI] 0.788–0.817 and 0.807, 95% CI 0.788–0.826) in the derivation and validation cohorts. The calibration plot demonstrated adequate calibration (2.5‐year area under the curve was 81.2 [79.1–83.5]). While hazard ratios for new‐onset diabetes with a weight‐loss therapy were comparable across risk groups (annual risks of <1%, 1%–5%, and >5%), there was a sixfold gradient in absolute risk reduction from lowest to highest risk group (p = 0.027).The developed clinical risk model effectively predicts new‐onset diabetes, with potential implications for personalized patient care and therapeutic decision making. [ABSTRACT FROM AUTHOR]
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- 2024
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27. The hidden genomic landscape of acute myeloid leukemia: Subclonal structure revealed by undetected mutations
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Corrado Tarella, Ilaria Iacobucci, Alessandro Cignetti, Pier Giuseppe Pelicci, Domenico Sardella, Giovanni Martinelli, Giorgio E. M. Melloni, Chiara Ronchini, Lucilla Luzi, Loris Bernard, Anna Di Russo, Anna Candoni, Francesco Lo-Coco, Luciano Giacò, Renato Fanin, Syed Khizer Hasan, Margherita Bodini, Serena Lavorgna, Laura Riva, Eleonora Toffoletti, Tiziana Ottone, Sara Volorio, Bodini, Margherita, Ronchini, Chiara, Giacò, Luciano, Russo, Anna, Melloni, Giorgio E. M., Luzi, Lucilla, Sardella, Domenico, Volorio, Sara, Hasan, Syed K., Ottone, Tiziana, Lavorgna, Serena, Lo-Coco, Francesco, Candoni, Anna, Fanin, Renato, Toffoletti, Eleonora, Iacobucci, Ilaria, Martinelli, Giovanni, Cignetti, Alessandro, Tarella, Corrado, Bernard, Lori, Pelicci, Pier Giuseppe, and Riva, Laura
- Subjects
Myeloid ,Immunology ,DNA Mutational Analysis ,Genomics ,Biology ,Acute ,Genome ,Biochemistry ,DNA sequencing ,DNA Mutational Analysi ,Databases ,Gene Frequency ,medicine ,Humans ,Allele frequency ,Genetics ,Leukemia ,Nucleic Acid ,Genome, Human ,Medicine (all) ,Myeloid leukemia ,Computational Biology ,High-Throughput Nucleotide Sequencing ,Hematology ,Cell Biology ,medicine.disease ,Leukemia, Myeloid, Acute ,Databases, Nucleic Acid ,Mutation ,medicine.anatomical_structure ,Genomic ,Human genome ,Settore MED/15 - Malattie del Sangue ,Perspectives ,Human - Abstract
The analyses carried out using 2 different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 acute myeloid leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these 2 variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and 1 secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of next-generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.
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- 2015
28. Lipoprotein(a), C-Reactive Protein, and Cardiovascular Risk in Primary and Secondary Prevention Populations.
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Small AM, Pournamdari A, Melloni GEM, Scirica BM, Bhatt DL, Raz I, Braunwald E, Giugliano RP, Sabatine MS, Peloso GM, Marston NA, and Natarajan P
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- Humans, Atherosclerosis epidemiology, Cohort Studies, Heart Disease Risk Factors, Ischemic Stroke, Myocardial Infarction epidemiology, Myocardial Infarction prevention & control, Risk Factors, Secondary Prevention, C-Reactive Protein analysis, Cardiovascular Diseases epidemiology, Lipoprotein(a) blood
- Abstract
Importance: Elevated lipoprotein(a) (Lp[a]) is a putative causal risk factor for atherosclerotic cardiovascular disease (ASCVD). There are conflicting data as to whether Lp(a) may increase cardiovascular risk only in the presence of concomitant inflammation., Objective: To investigate whether Lp(a) is associated with cardiovascular risk independent of high-sensitivity C-reactive protein (hs-CRP) in both primary and secondary prevention populations., Design, Setting, and Participants: This cohort study uses data from 3 distinct cohorts, 1 population-based cohort and 2 randomized clinical trials. Participants included individuals from the UK Biobank (data from 2006-2010) without prevalent ASCVD, participants in the FOURIER (TIMI 59) trial (data from 2013-2017) who had baseline Lp(a) and hs-CRP data, and participants in the SAVOR-TIMI 53 trial (data from 2010-2013) who had prevalent ASCVD and baseline values for Lp(a) and hs-CRP. The data analysis took place from November 2022 to November 2023., Exposure: Baseline plasma Lp(a), considered either as a continuous variable or dichotomized at 125 nmol/L., Main Outcomes and Measures: Risk of major adverse cardiovascular events (MACE) (composite of cardiovascular death, myocardial infarction [MI], or ischemic stroke), the individual MACE components, and peripheral artery disease (PAD)., Results: Among 357 220 individuals in the UK Biobank without prevalent ASCVD, 232 699 (65%) had low hs-CRP (<2 mg/L), and 124 521 (35%) had high hs-CRP (≥2 mg/L) values. In a Cox proportional hazard model adjusted for ASCVD risk factors, higher Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP value for MACE (hs-CRP ≥2 mg/L: hazard ratio [HR] per 50-nmol/L higher Lp[a], 1.05; 95% CI, 1.04-1.07; P < .001; for hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.04-1.07; P < .001; P = .80 for interaction), as well as MI, ischemic stroke, and PAD individually. Among 34 020 individuals in the FOURIER and SAVOR trials with baseline cardiometabolic disease, there were 17 643 (52%) with low and 16 377 (48%) with high baseline hs-CRP values. In Cox proportional hazard models using aggregated data from FOURIER and SAVOR, higher baseline Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP for MACE (hs-CRP ≥2 mg/L: HR per 50-nmol/L higher Lp[a], 1.02; 95% CI, 1.00-1.05; P = .04; hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.02-1.08; P < .001; P = .16 for interaction), MI, and PAD., Conclusions and Relevance: In this study, higher levels of Lp(a) were associated with MACE, MI, and PAD in both primary and secondary prevention populations regardless of baseline hs-CRP value.
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- 2024
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29. Per-Particle Cardiovascular Risk of Lipoprotein(a) vs Non-Lp(a) Apolipoprotein B-Containing Lipoproteins.
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Marston NA, Melloni GEM, Murphy SA, Morze J, Kamanu FK, Ellinor PT, Ruff CT, and Sabatine MS
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- Humans, Risk Factors, Apolipoproteins B, Lipoprotein(a), Cardiovascular Diseases epidemiology, Cardiovascular Diseases etiology
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- 2024
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30. Genetic Risk Score to Identify Risk of Venous Thromboembolism in Patients With Cardiometabolic Disease.
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Marston NA, Melloni GEM, Gurmu Y, Bonaca MP, Kamanu FK, Roselli C, Lee C, Cavallari I, Giugliano RP, Scirica BM, Bhatt DL, Steg PG, Cohen M, Storey RF, Keech AC, Raz I, Mosenzon O, Braunwald E, Lubitz SA, Ellinor PT, Sabatine MS, and Ruff CT
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- Aged, Female, Humans, Male, Metabolic Syndrome pathology, Middle Aged, Proportional Hazards Models, Proprotein Convertase 9 genetics, Risk Factors, Venous Thromboembolism etiology, Metabolic Syndrome genetics, Venous Thromboembolism diagnosis
- Abstract
Background: Venous thromboembolism (VTE) is a major cause of cardiovascular morbidity and mortality and has a known genetic contribution. We tested the performance of a genetic risk score for its ability to predict VTE in 3 cohorts of patients with cardiometabolic disease., Methods: We included patients from the FOURIER (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Patients With Elevated Risk), PEGASUS-TIMI 54 (Prevention of Cardiovascular Events in Patients With Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin), and SAVOR-TIMI 53 (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus) trials (history of a major atherosclerotic cardiovascular event, myocardial infarction, and diabetes, respectively) who consented for genetic testing and were not on baseline anticoagulation. We calculated a VTE genetic risk score based on 297 single nucleotide polymorphisms with established genome-wide significance. Patients were divided into tertiles of genetic risk. Cox proportional hazards models were used to calculate hazard ratios for VTE across genetic risk groups. The polygenic risk score was compared with available clinical risk factors (age, obesity, smoking, history of heart failure, and diabetes) and common monogenic mutations., Results: A total of 29 663 patients were included in the analysis with a median follow-up of 2.4 years, of whom 174 had a VTE event. There was a significantly increased gradient of risk across VTE genetic risk tertiles ( P -trend <0.0001). After adjustment for clinical risk factors, patients in the intermediate and high genetic risk groups had a 1.88-fold (95% CI, 1.23-2.89; P =0.004) and 2.70-fold (95% CI, 1.81-4.06; P <0.0001) higher risk of VTE compared with patients with low genetic risk. In a continuous model adjusted for clinical risk factors, each standard deviation increase in the genetic risk score was associated with a 47% (95% CI, 29-68) increased risk of VTE ( P <0.0001)., Conclusions: In a broad spectrum of patients with cardiometabolic disease, a polygenic risk score is a strong, independent predictor of VTE after accounting for available clinical risk factors, identifying 1/3 of patients who have a risk of VTE comparable to that seen with established monogenic thrombophilia.
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- 2021
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31. The Effect of PCSK9 (Proprotein Convertase Subtilisin/Kexin Type 9) Inhibition on the Risk of Venous Thromboembolism.
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Marston NA, Gurmu Y, Melloni GEM, Bonaca M, Gencer B, Sever PS, Pedersen TR, Keech AC, Roselli C, Lubitz SA, Ellinor PT, O'Donoghue ML, Giugliano RP, Ruff CT, and Sabatine MS
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- Aged, Antibodies, Monoclonal, Humanized adverse effects, Anticholesteremic Agents adverse effects, Biomarkers blood, Clinical Trials as Topic, Dyslipidemias blood, Dyslipidemias diagnosis, Dyslipidemias epidemiology, Female, Humans, Incidence, Male, Middle Aged, Pulmonary Embolism diagnosis, Pulmonary Embolism epidemiology, Pulmonary Embolism prevention & control, Risk Assessment, Risk Factors, Serine Proteinase Inhibitors adverse effects, Time Factors, Treatment Outcome, Venous Thromboembolism diagnosis, Venous Thromboembolism epidemiology, Venous Thrombosis diagnosis, Venous Thrombosis epidemiology, Antibodies, Monoclonal, Humanized therapeutic use, Anticholesteremic Agents therapeutic use, Cholesterol, LDL blood, Dyslipidemias drug therapy, Lipoprotein(a) blood, PCSK9 Inhibitors, Serine Proteinase Inhibitors therapeutic use, Venous Thromboembolism prevention & control, Venous Thrombosis prevention & control
- Abstract
Background: The relationship between cholesterol levels and risk of venous thromboembolism (VTE) is uncertain. We set out to determine the effect of PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibition on the risk of VTE, explore potential mechanisms, and examine the efficacy in subgroups with clinically and genetically defined risk., Methods: We performed a post hoc analysis of the FOURIER trial (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk) testing whether evolocumab reduces the risk of VTE events (deep venous thrombosis or pulmonary embolism). Data from FOURIER and ODYSSEY OUTCOMES (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment with Alirocumab) were then combined in a meta-analysis to assess the class effect of PCSK9 inhibition on the risk of VTE. We also analyzed baseline lipids in FOURIER to investigate potential mechanisms explaining the reduction in VTE with evolocumab. Last, an exploratory genetic analysis was performed in FOURIER to determine whether a VTE polygenic risk score could identify high-risk patients who would derive the greatest VTE reduction from evolocumab., Results: In FOURIER, the hazard ratio (HR) for VTE with evolocumab was 0.71 (95% CI, 0.50-1.00; P =0.05), with no effect in the 1st year (HR, 0.96 [95% CI, 0.57-1.62]) but a 46% reduction (HR, 0.54 [95% CI, 0.33-0.88]; P =0.014) beyond 1 year. A meta-analysis of FOURIER and ODYSSEY OUTCOMES demonstrated a 31% relative risk reduction in VTE with PCSK9 inhibition (HR, 0.69 [95% CI, 0.53-0.90]; P =0.007). There was no relation between baseline low-density lipoprotein cholesterol levels and magnitude of VTE risk reduction. In contrast, in patients with higher baseline lipoprotein(a) (Lp[a]) levels, evolocumab reduced Lp(a) by 33 nmol/L and risk of VTE by 48% (HR, 0.52 [95% CI, 0.30-0.89]; P =0.017), whereas, in patients with lower baseline Lp(a) levels, evolocumab reduced Lp(a) by only 7 nmol/L and had no effect on VTE risk ( P
interaction 0.087 for HR; Pheterogeneity 0.037 for absolute risk reduction). Modeled as a continuous variable, there was a significant interaction between baseline Lp(a) concentration and magnitude of VTE risk reduction ( Pinteraction =0.04). A polygenic risk score identified patients who were at >2-fold increased risk for VTE and who derived greater relative ( Pinteraction =0.04) and absolute VTE reduction ( Pheterogeneity =0.009) in comparison with those without high genetic risk., Conclusions: PCSK9 inhibition significantly reduces the risk of VTE. Lp(a) reduction may be an important mediator of this effect, a finding of particular interest given the ongoing development of potent Lp(a) inhibitors.- Published
- 2020
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32. Precision Trial Drawer, a Computational Tool to Assist Planning of Genomics-Driven Trials in Oncology.
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Melloni GEM, Guida A, Curigliano G, Botteri E, Esposito A, Kamal M, Le Tourneau C, Riva L, Magi A, de Maria R, Pelicci P, and Mazzarella L
- Abstract
Purpose: Trials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects., Methods: We developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated with the rarer mutation. We used data from The Cancer Genome Atlas to show their potential in a 10-arm imaginary trial of multiple cancers on the basis of genetic alterations suggested by the past Molecular Analysis for Personalised Therapy (MAP) conference. We validated PTD predictions versus real data from the SHIVA (A Randomized Phase II Trial Comparing Therapy Based on Tumor Molecular Profiling Versus Conventional Therapy in Patients With Refractory Cancer) trial., Results: In the MAP imaginary trial, PTD-optimized allocation reduces number of patients needed to molecularly screen by up to 71.8% (3.5 times) compared with nonoptimal trial designs. In the SHIVA trial, PTD correctly predicted the fraction of patients with actionable alterations (33.51% [95% CI, 29.4% to 37.6%] in imaginary v 32.92% [95% CI, 28.2% to 37.6%] expected) and allocation to specific treatment groups (RAS/MEK, PI3K/mTOR, or both)., Conclusion: PTD correctly predicts crucial parameters for the design of multiarm genetic biomarker-driven trials. PTD is available as a package in the R programming language and as an open-access Web-based app. It represents a useful resource for the community of precision oncology trialists. The Web-based app is available at https://gmelloni.github.io/ptd/shinyapp.html.
- Published
- 2018
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