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3. Lineage Specificity of Gene Expression Patterns

Author

Kluger, Yuval, Tuck, David P., Chang, Joseph T., Nakayama, Yasuhiro, Poddar, Ranjana, Kohya, Naohiko, Lian, Zheng, Ben Nasr, Abdelhakim, Halaban, H. Ruth, Krause, Diane S., Zhang, Xueqing, Newburger, Peter E., and Weissman, Sherman M.

Published
2004

4. Baseline correlates of frailty and its association with survival in United States veterans with acute myeloid leukemia.

Author

La, Jennifer, Lee, Michelle H., Brophy, Mary T., Do, Nhan V., Driver, Jane A., Tuck, David P., Fillmore, Nathanael R., and Dumontier, Clark

Subjects
ACUTE myeloid leukemia, FRAILTY, ELECTRONIC health records, SURVIVAL rate
Abstract

Frailty is an important construct to measure in acute myeloid leukemia (AML). We used the Veterans Affairs Frailty Index (VA-FI) – calculated using readily available data within the VA's electronic health records – to measure frailty in U.S. veterans with AML. Of the 1166 newly diagnosed and treated veterans with AML between 2012 and 2022, 722 (62%) veterans with AML were classified as frail (VA-FI > 0.2). At a median follow-up of 252.5 days, moderate-severely frail veterans had significantly worse survival than mildly frail, and non-frail veterans (median survival 179 vs. 306 vs. 417 days, p <.001). Increasing VA-FI severity was associated with higher mortality. A model with VA-FI in addition to the European LeukemiaNet (ELN) risk classification and other covariates statistically outperformed a model containing the ELN risk and other covariates alone (p <.001). These findings support the VA-FI as a tool to expand frailty measurement in research and clinical practice for informing prognosis in veterans with AML. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Loss of the retinoblastoma binding protein 2 (RBP2) histone demethylase suppresses tumorigenesis in mice lacking Rb1 or Men1

Author

Lin, Wenchu, Cao, Jian, Liu, Jiayun, Beshiri, Michael L., Fujiwara, Yuko, Francis, Joshua, Cherniack, Andrew D., Geisen, Christoph, Blair, Lauren P., Zou, Mike R., Shen, Xiaohua, Kawamori, Dan, Liu, Zongzhi, Grisanzio, Chiara, Watanabe, Hideo, Minamishima, Yoji Andrew, Zhang, Qing, Kulkarni, Rohit N., Signoretti, Sabina, Rodig, Scott J., Bronson, Roderick T., Orkin, Stuart H., Tuck, David P., Benevolenskaya, Elizaveta V., Meyerson, Matthew, Kaelin, William G., and Yan, Qin

Published
2011

6. Association of COVID-19 Vaccination With SARS-CoV-2 Infection in Patients With Cancer

Author

Wu, Julie Tsu-Yu, La, Jennifer, Branch-Elliman, Westyn, Huhmann, Linden B., Han, Summer S., Parmigiani, Giovanni, Tuck, David P., Brophy, Mary T., Do, Nhan V., Lin, Albert Y., Munshi, Nikhil C., and Fillmore, Nathanael R.

Subjects
Adult, Male, COVID-19 Vaccines, SARS-CoV-2, viruses, Research, Brief Report, fungi, Vaccination, COVID-19, respiratory tract diseases, body regions, Cohort Studies, Neoplasms, Online First, Humans, skin and connective tissue diseases, Comments, Aged, Retrospective Studies, Veterans
Abstract

This cohort study assesses the association between SARS-CoV-2 vaccination and SARS-CoV-2 infections among a population of Veterans Affairs (VA) patients with cancer., Key Points Question What is the effectiveness of SARS-CoV-2 vaccination in patients with cancer? Findings In this cohort study of US Veterans Affairs patients who received systemic therapy for cancer between August 15, 2010, and May 4, 2021, a proxy measure for effectiveness of the vaccine starting 14 days after the second dose was 58%. The measure of effectiveness starting 14 days after the second dose was 85% in patients who had not received systemic therapy within the 6 months prior to vaccination and 76% among those receiving hormonal treatment. Meaning Results suggest that SARS-CoV-2 vaccination associated with lower infection rates in patients with cancer, especially in those not receiving current systemic therapy and those receiving hormonal treatment., Importance Patients with cancer are at increased risk for severe COVID-19, but it is unknown whether SARS-CoV-2 vaccination is effective for them. Objective To determine the association between SARS-CoV-2 vaccination and SARS-CoV-2 infections among a population of Veterans Affairs (VA) patients with cancer. Design, Setting, and Participants Retrospective, multicenter, nationwide cohort study of SARS-CoV-2 vaccination and infection among patients in the VA health care system from December 15, 2020, to May 4, 2021. All adults with solid tumors or hematologic cancer who received systemic cancer-directed therapy from August 15, 2010, to May 4, 2021, and were alive and without a documented SARS-CoV-2 positive result as of December 15, 2020, were eligible for inclusion. Each day between December 15, 2020, and May 4, 2021, newly vaccinated patients were matched 1:1 with unvaccinated or not yet vaccinated controls based on age, race and ethnicity, VA facility, rurality of home address, cancer type, and treatment type/timing. Exposures Receipt of a SARS-CoV-2 vaccine. Main Outcomes and Measures The primary outcome was documented SARS-CoV-2 infection. A proxy for vaccine effectiveness was defined as 1 minus the risk ratio of SARS-CoV-2 infection for vaccinated individuals compared with unvaccinated controls. Results A total of 184 485 patients met eligibility criteria, and 113 796 were vaccinated. Of these, 29 152 vaccinated patients (median [IQR] age, 74.1 [70.2-79.3] years; 95% were men; 71% were non-Hispanic White individuals) were matched 1:1 to unvaccinated or not yet vaccinated controls. As of a median 47 days of follow-up, 436 SARS-CoV-2 infections were detected in the matched cohort (161 infections in vaccinated patients vs 275 in unvaccinated patients). There were 17 COVID-19–related deaths in the vaccinated group vs 27 COVID-19–related deaths in the unvaccinated group. Overall vaccine effectiveness in the matched cohort was 58% (95% CI, 39% to 72%) starting 14 days after the second dose. Patients who received chemotherapy within 3 months prior to the first vaccination dose were estimated to have a vaccine effectiveness of 57% (95% CI, –23% to 90%) starting 14 days after the second dose vs 76% (95% CI, 50% to 91%) for those receiving endocrine therapy and 85% (95% CI, 29% to 100%) for those who had not received systemic therapy for at least 6 months prior. Conclusions and Relevance In this cohort study, COVID-19 vaccination was associated with lower SARS-CoV-2 infection rates in patients with cancer. Some immunosuppressed subgroups may remain at early risk for COVID-19 despite vaccination, and consideration should be given to additional risk reduction strategies, such as serologic testing for vaccine response and a third vaccine dose to optimize outcomes.

Published
2021

8. Phase 1b study of the mammalian target of rapamycin inhibitor sirolimus in combination with nanoparticle albumin–bound paclitaxel in patients with advanced solid tumors

Author

Abu-Khalaf, Maysa M., Baumgart, Megan A., Gettinger, Scott N., Doddamane, Indukala, Tuck, David P., Hou, Shihe, Chen, Nianhang, Sullivan, Catherine, Lezon-Geyda, Kimberly, Zelterman, Daniel, Hatzis, Christos, Deshpande, Hari, Digiovanna, Michael P., Azodi, Masoud, Schwartz, Peter E., and Harris, Lyndsay N.

Published
2015
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9. PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer

Author

Martín, Miguel, Prat, Aleix, Rodríguez-Lescure, Álvaro, Caballero, Rosalía, Ebbert, Mark T. W., Munárriz, Blanca, Ruiz-Borrego, Manuel, Bastien, Roy R. L., Crespo, Carmen, Davis, Carole, Rodríguez, César A., López-Vega, José M., Furió, Vicente, García, Ana M., Casas, Maribel, Ellis, Matthew J., Berry, Donald A., Pitcher, Brandelyn N., Harris, Lyndsay, Ruiz, Amparo, Winer, Eric, Hudis, Clifford, Stijleman, Inge J., Tuck, David P., Carrasco, Eva, Perou, Charles M., and Bernard, Philip S.

Published
2013
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12. Prognostic value of the veterans affairs frailty index in older patients with non‐small cell lung cancer.

Author

Cheng, David, Dumontier, Clark, Sheikh, Ayesha R., La, Jennifer, Brophy, Mary T., Do, Nhan V., Driver, Jane A., Tuck, David P., and Fillmore, Nathanael R.

Subjects
NON-small-cell lung carcinoma, PROGNOSIS, OLDER patients, FRAILTY, ELECTRONIC health records
Abstract

Background: Older patients with non‐small cell lung cancer (NSCLC) are a heterogeneous population with varying degrees of frailty. An electronic frailty index such as the Veterans Affairs Frailty Index (VA‐FI) can potentially help identify vulnerable patients at high risk of poor outcomes. Methods: NSCLC patients ≥65 years old and diagnosed in 2002–2017 were identified using the VA Central Cancer Registry. The VA‐FI was calculated using administrative codes from VA electronic health records data linked with Medicare and Medicaid data. We assessed associations between the VA‐FI and times to mortality, hospitalization, and emergency room (ER) visit following diagnosis by Kaplan–Meier analysis and multivariable stratified Cox models. We also evaluated the change in discrimination and calibration of reference prognostic models after adding VA‐FI. Results: We identified a cohort of 42,204 older NSCLC VA patients, in which 55.5% were classified as frail (VA‐FI >0.2). After adjustment, there was a strong association between VA‐FI and the risk of mortality (HR = 1.23 for an increase of four deficits or, equivalently, an increase of 0.129 on VA‐FI, p < 0.001), hospitalization (HR = 1.16 for four deficits, p < 0.001), and ER visit (HR = 1.18 for four deficits, p < 0.001). Adding VA‐FI to baseline prognostic models led to statistically significant improvements in time‐dependent area under curves and did not have a strong impact on calibration. Conclusion: Older NSCLC patients with higher VA‐FI have significantly elevated risks of mortality, hospitalizations, and ER visits following diagnosis. An electronic frailty index can serve as an accessible tool to identify patients with vulnerabilities to inform clinical care and research. [ABSTRACT FROM AUTHOR]

Published
2022
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20. Prevalence and Outcome of COVID-19 Infection in Cancer Patients: A National Veterans Affairs Study.

Author

Fillmore, Nathanael R, La, Jennifer, Szalat, Raphael E, Tuck, David P, Nguyen, Vinh, Yildirim, Cenk, Do, Nhan V, Brophy, Mary T, and Munshi, Nikhil C

Subjects
COVID-19, CANCER patients, CANCER-related mortality, VETERANS, OLDER patients, RESEARCH funding
Abstract

Background: Emerging data suggest variability in susceptibility and outcome to coronavirus disease 2019 (COVID-19) infection. Identifying risk factors associated with infection and outcomes in cancer patients is necessary to develop healthcare recommendations.Methods: We analyzed electronic health records of the US Veterans Affairs Healthcare System and assessed the prevalence of COVID-19 infection in cancer patients. We evaluated the proportion of cancer patients tested for COVID-19 who were positive, as well as outcome attributable to COVID-19, and stratified by clinical characteristics including demographics, comorbidities, cancer treatment, and cancer type. All statistical tests are 2-sided.Results: Of 22 914 cancer patients tested for COVID-19, 1794 (7.8%) were positive. The prevalence of COVID-19 was similar across age. Higher prevalence was observed in African American (15.0%) compared with White (5.5%; P < .001) and in patients with hematologic malignancy compared with those with solid tumors (10.9% vs 7.8%; P < .001). Conversely, prevalence was lower in current smokers and patients who recently received cancer therapy (<6 months). The COVID-19-attributable mortality was 10.9%. Higher attributable mortality rates were observed in older patients, those with higher Charlson comorbidity score, and in certain cancer types. Recent (<6 months) or past treatment did not influence attributable mortality. Importantly, African American patients had 3.5-fold higher COVID-19-attributable hospitalization; however, they had similar attributable mortality as White patients.Conclusion: Preexistence of cancer affects both susceptibility to COVID-19 infection and eventual outcome. The overall COVID-19-attributable mortality in cancer patients is affected by age, comorbidity, and specific cancer types; however, race or recent treatment including immunotherapy do not impact outcome. [ABSTRACT FROM AUTHOR]

Published
2021
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21. Characterizing disease states from topological properties of transcriptional regulatory networks

Author

Kluger Harriet M, Tuck David P, and Kluger Yuval

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. Results We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. Conclusion Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.

Published
2006
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24. Preclinical Activity of IRAK4 Kinase Inhibitor CA-4948 Alone or in Combination with Targeted Therapies and Preliminary Phase 1 Clinical Results in Non-Hodgkin Lymphoma

Author

Booher, Robert N, Nowakowski, Grzegorz S., Patel, Krish, Lunning, Matthew A., Samson, Maria Elena S., Atoyan, Ruzanna, Ma, Anna W, Xu, Guang-Xin, Dellarocca, Steven, Modafferi, Holly, Borek, Mylissa, Zhang, Zhidong, Parker, Jefferson, Whitney, Duncan, Wang, Hongwei, Tuck, David P, and Younes, Anas

Published
2018
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25. Socialism and the economy.

Author

Tuck, David

Subjects
SOCIALISM, MIXED economy, DEMOCRATIC socialism, POLITICAL science writing, POLITICAL elites
Published
2020

27. An introduction to the Enlightenment.

Author

Tuck, David

Subjects
ENLIGHTENMENT, AMERICAN Revolutionary War, 1775-1783, SEVENTEENTH century, HUMAN beings, INDUSTRIAL revolution
Published
2019

30. 31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016): part two: National Harbor, MD, USA. 9-13 November 2016

Author

Ager, Casey, Reilley, Matthew, Nicholas, Courtney, Bartkowiak, Todd, Jaiswal, Ashvin, Curran, Michael, Albershardt, Tina C., Bajaj, Anshika, Archer, Jacob F., Reeves, Rebecca S., Ngo, Lisa Y., Berglund, Peter, ter Meulen, Jan, Denis, Caroline, Ghadially, Hormas, Arnoux, Thomas, Chanuc, Fabien, Fuseri, Nicolas, Wilkinson, Robert W., Wagtmann, Nicolai, Morel, Yannis, Andre, Pascale, Atkins, Michael B., Carlino, Matteo S., Ribas, Antoni, Thompson, John A., Choueiri, Toni K., Hodi, F. Stephen, Hwu, Wen-Jen, McDermott, David F., Atkinson, Victoria, Cebon, Jonathan S., Fitzharris, Bernie, Jameson, Michael B., McNeil, Catriona, Hill, Andrew G., Mangin, Eric, Ahamadi, Malidi, van Vugt, Marianne, van Zutphen, Mariëlle, Ibrahim, Nageatte, Long, Georgina V., Gartrell, Robyn, Blake, Zoe, Simoes, Ines, Fu, Yichun, Saito, Takuro, Qian, Yingzhi, Lu, Yan, Saenger, Yvonne M., Budhu, Sadna, De Henau, Olivier, Zappasodi, Roberta, Schlunegger, Kyle, Freimark, Bruce, Hutchins, Jeff, Barker, Christopher A., Wolchok, Jedd D., Merghoub, Taha, Burova, Elena, Allbritton, Omaira, Hong, Peter, Dai, Jie, Pei, Jerry, Liu, Matt, Kantrowitz, Joel, Lai, Venus, Poueymirou, William, MacDonald, Douglas, Ioffe, Ella, Mohrs, Markus, Olson, William, Thurston, Gavin, Capasso, Cristian, Frascaro, Federica, Carpi, Sara, Tähtinen, Siri, Feola, Sara, Fusciello, Manlio, Peltonen, Karita, Martins, Beatriz, Sjöberg, Madeleine, Pesonen, Sari, Ranki, Tuuli, Kyruk, Lukasz, Ylösmäki, Erkko, Cerullo, Vincenzo, Cerignoli, Fabio, Xi, Biao, Guenther, Garret, Yu, Naichen, Muir, Lincoln, Zhao, Leyna, Abassi, Yama, Cervera-Carrascón, Víctor, Siurala, Mikko, Santos, João, Havunen, Riikka, Parviainen, Suvi, Hemminki, Akseli, Dalgleish, Angus, Mudan, Satvinder, DeBenedette, Mark, Plachco, Ana, Gamble, Alicia, Grogan, Elizabeth W., Krisko, John, Tcherepanova, Irina, Nicolette, Charles, Dhupkar, Pooja, Yu, Ling, Kleinerman, Eugenie S., Gordon, Nancy, Grenga, Italia, Lepone, Lauren, Gameiro, Sofia, Knudson, Karin M., Fantini, Massimo, Tsang, Kwong, Hodge, James, Donahue, Renee, Schlom, Jeffrey, Evans, Elizabeth, Bussler, Holm, Mallow, Crystal, Reilly, Christine, Torno, Sebold, Scrivens, Maria, Foster, Cathie, Howell, Alan, Balch, Leslie, Knapp, Alyssa, Leonard, John E., Paris, Mark, Fisher, Terry, Hu-Lieskovan, Siwen, Smith, Ernest, Zauderer, Maurice, Fogler, William, Franklin, Marilyn, Thayer, Matt, Saims, Dan, Magnani, John L., Gong, Jian, Gray, Michael, Fromm, George, de Silva, Suresh, Giffin, Louise, Xu, Xin, Rose, Jason, Schreiber, Taylor H., Gameiro, Sofia R., Clavijo, Paul E., Allen, Clint T., Hodge, James W., Tsang, Kwong Y., Grogan, Jane, Manieri, Nicholas, Chiang, Eugene, Caplazi, Patrick, Yadav, Mahesh, Hagner, Patrick, Chiu, Hsiling, Waldman, Michelle, Klippel, Anke, Thakurta, Anjan, Pourdehnad, Michael, Gandhi, Anita, Henrich, Ian, Quick, Laura, Young, Rob, Chou, Margaret, Hotson, Andrew, Willingham, Stephen, Ho, Po, Choy, Carmen, Laport, Ginna, McCaffery, Ian, Miller, Richard, Tipton, Kimberly A., Wong, Kenneth R., Singson, Victoria, Wong, Chihunt, Chan, Chanty, Huang, Yuanhiu, Liu, Shouchun, Richardson, Jennifer H., Kavanaugh, W. Michael, West, James, Irving, Bryan A., Jaini, Ritika, Loya, Matthew, Eng, Charis, Johnson, Melissa L., Adjei, Alex A., Opyrchal, Mateusz, Ramalingam, Suresh, Janne, Pasi A., Dominguez, George, Gabrilovich, Dmitry, de Leon, Laura, Hasapidis, Jeannette, Diede, Scott J., Ordentlich, Peter, Cruickshank, Scott, Meyers, Michael L., Hellmann, Matthew D., Kalinski, Pawel, Zureikat, Amer, Edwards, Robert, Muthuswamy, Ravi, Obermajer, Nataša, Urban, Julie, Butterfield, Lisa H., Gooding, William, Zeh, Herbert, Bartlett, David, Zubkova, Olga, Agapova, Larissa, Kapralova, Marina, Krasovskaia, Liudmila, Ovsepyan, Armen, Lykov, Maxim, Eremeev, Artem, Bokovanov, Vladimir, Grigoryeva, Olga, Karpov, Andrey, Ruchko, Sergey, Shuster, Alexandr, Khalil, Danny N., Campesato, Luis Felipe, Li, Yanyun, Lazorchak, Adam S., Patterson, Troy D., Ding, Yueyun, Sasikumar, Pottayil, Sudarshan, Naremaddepalli, Gowda, Nagaraj, Ramachandra, Raghuveer, Samiulla, Dodheri, Giri, Sanjeev, Eswarappa, Rajesh, Ramachandra, Murali, Tuck, David, Wyant, Timothy, Leshem, Jasmin, Liu, Xiu-fen, Bera, Tapan, Terabe, Masaki, Bossenmaier, Birgit, Niederfellner, Gerhard, Reiter, Yoram, Pastan, Ira, Xia, Leiming, Xia, Yang, Hu, Yangyang, Wang, Yi, Bao, Yangyi, Dai, Fu, Huang, Shiang, Hurt, Elaine, Hollingsworth, Robert E., Lum, Lawrence G., Chang, Alfred E., Wicha, Max S., Li, Qiao, Mace, Thomas, Makhijani, Neil, Talbert, Erin, Young, Gregory, Guttridge, Denis, Conwell, Darwin, Lesinski, Gregory B., Gonzales, Rodney JM Macedo, Huffman, Austin P., Wang, Ximi K., Reshef, Ran, MacKinnon, Andy, Chen, Jason, Gross, Matt, Marguier, Gisele, Shwonek, Peter, Sotirovska, Natalija, Steggerda, Susanne, Parlati, Francesco, Makkouk, Amani, Bennett, Mark K., Emberley, Ethan, Huang, Tony, Li, Weiqun, Neou, Silinda, Pan, Alison, Zhang, Jing, Zhang, Winter, Marshall, Netonia, Marron, Thomas U., Agudo, Judith, Brown, Brian, Brody, Joshua, McQuinn, Christopher, Farren, Matthew, Komar, Hannah, Shakya, Reena, Ludwug, Thomas, Morillon, Y. Maurice, Hammond, Scott A., Greiner, John W., Nath, Pulak R., Schwartz, Anthony L., Maric, Dragan, Roberts, David D., Naing, Aung, Papadopoulos, Kyriakos P., Autio, Karen A., Wong, Deborah J., Patel, Manish, Falchook, Gerald, Pant, Shubham, Ott, Patrick A., Whiteside, Melinda, Patnaik, Amita, Mumm, John, Janku, Filip, Chan, Ivan, Bauer, Todd, Colen, Rivka, VanVlasselaer, Peter, Brown, Gail L., Tannir, Nizar M., Oft, Martin, Infante, Jeffrey, Lipson, Evan, Gopal, Ajay, Neelapu, Sattva S., Armand, Philippe, Spurgeon, Stephen, Leonard, John P., Sanborn, Rachel E., Melero, Ignacio, Gajewski, Thomas F., Maurer, Matthew, Perna, Serena, Gutierrez, Andres A., Clynes, Raphael, Mitra, Priyam, Suryawanshi, Satyendra, Gladstone, Douglas, Callahan, Margaret K., Crooks, James, Brown, Sheila, Gauthier, Audrey, de Boisferon, Marc Hillairet, MacDonald, Andrew, Brunet, Laura Rosa, Rothwell, William T., Bell, Peter, Wilson, James M., Sato-Kaneko, Fumi, Yao, Shiyin, Zhang, Shannon S., Carson, Dennis A., Guiducci, Cristina, Coffman, Robert L., Kitaura, Kazutaka, Matsutani, Takaji, Suzuki, Ryuji, Hayashi, Tomoko, Cohen, Ezra E. W., Schaer, David, Li, Yanxia, Dobkin, Julie, Amatulli, Michael, Hall, Gerald, Doman, Thompson, Manro, Jason, Dorsey, Frank Charles, Sams, Lillian, Holmgaard, Rikke, Persaud, Krishnadatt, Ludwig, Dale, Surguladze, David, Kauh, John S., Novosiadly, Ruslan, Kalos, Michael, Driscoll, Kyla, Pandha, Hardev, Ralph, Christy, Harrington, Kevin, Curti, Brendan, Akerley, Wallace, Gupta, Sumati, Melcher, Alan, Mansfield, David, Kaufman, David R., Schmidt, Emmett, Grose, Mark, Davies, Bronwyn, Karpathy, Roberta, Shafren, Darren, Shamalov, Katerina, Cohen, Cyrille, Sharma, Naveen, Allison, James, Shekarian, Tala, Valsesia-Wittmann, Sandrine, Caux, Christophe, Marabelle, Aurelien, Slomovitz, Brian M., Moore, Kathleen M., Youssoufian, Hagop, Posner, Marshall, Tewary, Poonam, Brooks, Alan D., Xu, Ya-Ming, Wijeratne, Kithsiri, Gunatilaka, Leslie A. A., Sayers, Thomas J., Vasilakos, John P., Alston, Tesha, Dovedi, Simon, Elvecrog, James, Grigsby, Iwen, Herbst, Ronald, Johnson, Karen, Moeckly, Craig, Mullins, Stefanie, Siebenaler, Kristen, SternJohn, Julius, Tilahun, Ashenafi, Tomai, Mark A., Vogel, Katharina, Vietsch, Eveline E., Wellstein, Anton, Wythes, Martin, Crosignani, Stefano, Tumang, Joseph, Alekar, Shilpa, Bingham, Patrick, Cauwenberghs, Sandra, Chaplin, Jenny, Dalvie, Deepak, Denies, Sofie, De Maeseneire, Coraline, Feng, JunLi, Frederix, Kim, Greasley, Samantha, Guo, Jie, Hardwick, James, Kaiser, Stephen, Jessen, Katti, Kindt, Erick, Letellier, Marie-Claire, Li, Wenlin, Maegley, Karen, Marillier, Reece, Miller, Nichol, Murray, Brion, Pirson, Romain, Preillon, Julie, Rabolli, Virginie, Ray, Chad, Ryan, Kevin, Scales, Stephanie, Srirangam, Jay, Solowiej, Jim, Stewart, Al, Streiner, Nicole, Torti, Vince, Tsaparikos, Konstantinos, Zheng, Xianxian, Driessens, Gregory, Gomes, Bruno, Kraus, Manfred, Xu, Chunxiao, Zhang, Yanping, Kradjian, Giorgio, Qin, Guozhong, Qi, Jin, Xu, Xiaomei, Marelli, Bo, Yu, Huakui, Guzman, Wilson, Tighe, Rober, Salazar, Rachel, Lo, Kin-Ming, English, Jessie, Radvanyi, Laszlo, Lan, Yan, Postow, Michael, Senbabaoglu, Yasin, Gasmi, Billel, Zhong, Hong, Liu, Cailian, Hirschhorhn-Cymerman, Daniel, Zha, Yuanyuan, Malnassy, Gregory, Fulton, Noreen, Park, Jae-Hyun, Stock, Wendy, Nakamura, Yusuke, Liu, Hongtao, Ju, Xiaoming, Kosoff, Rachelle, Ramos, Kimberly, Coder, Brandon, Petit, Robert, Princiotta, Michael, Perry, Kyle, Zou, Jun, Arina, Ainhoa, Fernandez, Christian, Zheng, Wenxin, Beckett, Michael A., Mauceri, Helena J., Fu, Yang-Xin, Weichselbaum, Ralph R., Lewis, Whitney, Han, Yanyan, Wu, Yeting, Yang, Chou, Huang, Jing, Wu, Dongyun, Li, Jin, Liang, Xiaoling, Zhou, Xiangjun, Hou, Jinlin, Hassan, Raffit, Jahan, Thierry, Antonia, Scott J., Kindler, Hedy L., Alley, Evan W., Honarmand, Somayeh, Liu, Weiqun, Leong, Meredith L., Whiting, Chan C., Nair, Nitya, Enstrom, Amanda, Lemmens, Edward E., Tsujikawa, Takahiro, Kumar, Sushil, Coussens, Lisa M., Murphy, Aimee L., Brockstedt, Dirk G., Koch, Sven D., Sebastian, Martin, Weiss, Christian, Früh, Martin, Pless, Miklos, Cathomas, Richard, Hilbe, Wolfgang, Pall, Georg, Wehler, Thomas, Alt, Jürgen, Bischoff, Helge, Geissler, Michael, Griesinger, Frank, Kollmeier, Jens, Papachristofilou, Alexandros, Doener, Fatma, Fotin-Mleczek, Mariola, Hipp, Madeleine, Hong, Henoch S., Kallen, Karl-Josef, Klinkhardt, Ute, Stosnach, Claudia, Scheel, Birgit, Schroeder, Andreas, Seibel, Tobias, Gnad-Vogt, Ulrike, Zippelius, Alfred, Park, Ha-Ram, Ahn, Yong-Oon, Kim, Tae Min, Kim, Soyeon, Kim, Seulki, Lee, Yu Soo, Keam, Bhumsuk, Kim, Dong-Wan, Heo, Dae Seog, Pilon-Thomas, Shari, Weber, Amy, Morse, Jennifer, Kodumudi, Krithika, Liu, Hao, Mullinax, John, Sarnaik, Amod A., Pike, Luke, Bang, Andrew, Balboni, Tracy, Taylor, Allison, Spektor, Alexander, Wilhite, Tyler, Krishnan, Monica, Cagney, Daniel, Alexander, Brian, Aizer, Ayal, Buchbinder, Elizabeth, Awad, Mark, Ghandi, Leena, Schoenfeld, Jonathan, Lessey-Morillon, Elizabeth, Ridnour, Lisa, Segal, Neil H., Sharma, Manish, Le, Dung T., Ferris, Robert L., Zelenetz, Andrew D., Levy, Ronald, Lossos, Izidore S., Jacobson, Caron, Ramchandren, Radhakrishnan, Godwin, John, Colevas, A. 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I., Ross, Merrick, Agarwala, Sanjiv, Grossmann, Kenneth, Taylor, Matthew, Vetto, John, Neves, Rogerio, Daud, Adil, Khong, Hung, Meek, Stephanie M., Ungerleider, Richard, Welden, Scott, Tanaka, Maki, Williams, Matthew, Hallmeyer, Sigrun, Fox, Bernard, Feng, Zipei, Paustian, Christopher, Bifulco, Carlo, Zafar, Sadia, Hemminki, Otto, Bramante, Simona, Vassilev, Lotta, Wang, Hongjie, Lieber, Andre, Hemmi, Silvio, de Gruijl, Tanja, Kanerva, Anna, Ansari, Tameem, Sundararaman, Srividya, Roen, Diana, Lehmann, Paul, Bloom, Anja C., Bender, Lewis H., Walters, Ian B., Berzofsky, Jay A., Chapelin, Fanny, Ahrens, Eric T., DeFalco, Jeff, Harbell, Michael, Manning-Bog, Amy, Scholz, Alexander, Zhang, Danhui, Baia, Gilson, Tan, Yann Chong, Sokolove, Jeremy, Kim, Dongkyoon, Williamson, Kevin, Chen, Xiaomu, Colrain, Jillian, Santo, Gregg Espiritu, Nguyen, Ngan, Volkmuth, Wayne, Greenberg, Norman, Robinson, William, Emerling, Daniel, Drake, Charles G., Petrylak, Daniel P., Antonarakis, Emmanuel S., 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Kenneth R., Diab, Adi, Sharma, Padmanee, Hong, David, Welsh, James, Parsons, Andrea J., Leleux, Jardin, Ascarateil, Stephane, Koziol, Marie Eve, Bai, Dina, Dai, Peihong, Wang, Weiyi, Yang, Ning, Shuman, Stewart, Deng, Liang, Dillon, Patrick, Petroni, Gina, Brenin, David, Bullock, Kim, Olson, Walter, Smolkin, Mark E., Smith, Kelly, Nail, Carmel, Slingluff, Craig L., Sharma, Meenu, Fa’ak, Faisal, Janssen, Louise, Khong, Hiep, Xiao, Zhilan, Hailemichael, Yared, Singh, Manisha, Vianden, Christina, Overwijk, Willem W., Facciabene, Andrea, Stefano, Pierini, Chongyung, Fang, Rafail, Stavros, Nielsen, Michael, Vanderslice, Peter, Woodside, Darren G., Market, Robert V., Biediger, Ronald J., Marathi, Upendra K., Hollevoet, Kevin, Geukens, Nick, Declerck, Paul, Joly, Nathalie, McIntosh, Laura, Paramithiotis, Eustache, Rizell, Magnus, Sternby, Malin, Andersson, Bengt, Karlsson-Parra, Alex, Kuai, Rui, Ochyl, Lukasz, Schwendeman, Anna, Moon, James, Deng, Weiwen, Hudson, Thomas E., Hanson, Bill, Rae, Chris S., Burrill, Joel, Skoble, Justin, Katibah, George, deVries, Michele, Lauer, Peter, Dubensky, Thomas W., Chen, Xin, Zhou, Li, Ren, Xiubao, Aggarwal, Charu, Mangrolia, Drishty, Cohen, Roger, Weinstein, Gregory, Morrow, Matthew, Bauml, Joshua, Kraynyak, Kim, Boyer, Jean, Yan, Jian, Lee, Jessica, Humeau, Laurent, Oyola, Sandra, Duff, Susan, Weiner, David, Yang, Zane, Bagarazzi, Mark, McNeel, Douglas G., Eickhoff, Jens, Jeraj, Robert, Staab, Mary Jane, Straus, Jane, Rekoske, Brian, Liu, Glenn, Melssen, Marit, Grosh, William, Varhegyi, Nikole, Galeassi, Nadejda, Deacon, Donna H., Gaughan, Elizabeth, Ghisoli, Maurizio, Barve, Minal, Mennel, Robert, Wallraven, Gladice, Manning, Luisa, Senzer, Neil, Nemunaitis, John, Ogasawara, Masahiro, Ota, Shuichi, Peace, Kaitlin M., Hale, Diane F., Vreeland, Timothy J., Jackson, Doreen O., Berry, John S., Trappey, Alfred F., Herbert, Garth S., Clifton, Guy T., Hardin, Mark O., Toms, Anne, Qiao, Na, Litton, Jennifer, Peoples, George E., Mittendorf, 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H., Qiu, Xiaohong, Fraser, Kathryn, Jonas, Adria, Ottoson, Nadine, Gordon, Keith, Kangas, Takashi O., Leonardo, Steven, Ertelt, Kathleen, Walsh, Richard, Uhlik, Mark, Graff, Jeremy, Bose, Nandita, Gupta, Ravi, Mandloi, Nitin, Paul, Kiran, Patil, Ashwini, Sathian, Rekha, Mohan, Aparna, Manoharan, Malini, Chaudhuri, Amitabha, Chen, Yu, Lin, Jing, Ye, Yun-bin, Xu, Chun-wei, Chen, Gang, Guo, Zeng-qing, Komarov, Andrey, Chenchik, Alex, Makhanov, Michael, Frangou, Costa, Zheng, Yi, Coltharp, Carla, Unfricht, Darryn, Dilworth, Ryan, Fridman, Leticia, Liu, Linying, Rajopadhye, Milind, Miller, Peter, Concha-Benavente, Fernando, Bauman, Julie, Trivedi, Sumita, Srivastava, Raghvendra, Ohr, James, Heron, Dwight, Duvvuri, Uma, Kim, Seungwon, Torrey, Heather, Mera, Toshi, Okubo, Yoshiaki, Vanamee, Eva, Foster, Rosemary, Faustman, Denise, Stack, Edward, Izaki, Daisuke, Beck, Kristen, Jia, Dan Tong, Armenta, Paul, White-Stern, Ashley, Marks, Douglas, Taback, Bret, Horst, Basil, Glickman, Laura Hix, Kanne, David B., Gauthier, Kelsey S., Desbien, Anthony L., Francica, Brian, Leong, Justin L., Sung, Leonard, Metchette, Ken, Kasibhatla, Shailaja, Pferdekamper, Anne Marie, Zheng, Lianxing, Cho, Charles, Feng, Yan, McKenna, Jeffery M., Tallarico, John, Bender, Steven, Ndubaku, Chudi, McWhirter, Sarah M., Gugel, Elena Gonzalez, Bell, Charles J. 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L., Rapisuwon, Suthee, Radfar, Arash, Gardner, Kellie, Gibney, Geoffrey, Atkins, Michael, Rennier, Keith R., Crowder, Robert, Wang, Ping, Pachynski, Russell K., Carrero, Rosa M. Santana, Rivas, Sarai, Beceren-Braun, Figen, Anthony, Scott, Schluns, Kimberly S., Sawant, Deepali, Chikina, Maria, Yano, Hiroshi, Workman, Creg, Salerno, Elise, Mauldin, Ileana, Deacon, Donna, Shea, Sofia, Pinczewski, Joel, Gajewski, Thomas, Spranger, Stefani, Horton, Brendan, Suzuki, Akiko, Leland, Pamela, Joshi, Bharat H., Puri, Raj K., Sweis, Randy F., Bao, Riyue, Luke, Jason, Theodoraki, Marie-Nicole, Mogundo, Frances-Mary, Won, Haejung, Moreira, Dayson, Gao, Chan, Zhao, Xingli, Duttagupta, Priyanka, Jones, Jeremy, D’Apuzzo, Massimo, and Pal, Sumanta

Published
2016
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31. Molecular Phenotypes in Triple Negative Breast Cancer from African American Patients Suggest Targets for Therapy.

Author

Lindner, Robert, Sullivan, Catherine, Offor, Onyinye, Lezon-Geyda, Kimberly, Halligan, Kyle, Fischbach, Neal, Shah, Mansi, Bossuyt, Veerle, Schulz, Vincent, Tuck, David P., and Harris, Lyndsay N.

Subjects
PHENOTYPES, TRIPLE-negative breast cancer, DISEASES in African Americans, CELL proliferation, CANCER-related mortality, IMMUNOHISTOCHEMISTRY, RETROSPECTIVE studies
Abstract

Triple negative breast cancer (TNBC) is characterized by high proliferation, poor differentiation and a poor prognosis due to high rates of recurrence. Despite lower overall incidence African American (AA) patients suffer from higher breast cancer mortality in part due to the higher proportion of TNBC cases among AA patients compared to European Americans (EA). It was recently shown that the clinical heterogeneity of TNBC is reflected by distinct transcriptional programs with distinct drug response profiles in preclinical models. In this study, gene expression profiling and immunohistochemistry were used to elucidate potential differences between TNBC tumors of EA and AA patients on a molecular level. In a retrospective cohort of 136 TNBC patients, a major transcriptional signature of proliferation was found to be significantly upregulated in samples of AA ethnicity. Furthermore, transcriptional profiles of AA tumors showed differential activation of insulin-like growth factor 1 (IGF1) and a signature of BRCA1 deficiency in this cohort. Using signatures derived from the meta-analysis of TNBC gene expression carried out by Lehmann et al., tumors from AA patients were more likely of basal-like subtypes whereas transcriptional features of many EA samples corresponded to mesenchymal-like or luminal androgen receptor driven subtypes. These results were validated in The Cancer Genome Atlas mRNA and protein expression data, again showing enrichment of a basal-like phenotype in AA tumors and mesenchymal subtypes in EA tumors. In addition, increased expression of VEGF-activated genes together with elevated microvessel area determined by the AQUA method suggest that AA patients exhibit higher tumor vascularization. This study confirms the existence of distinct transcriptional programs in triple negative breast cancer in two separate cohorts and that these programs differ by racial group. Differences in TNBC subtypes and levels of tumor angiogenesis in AA versus EA patients suggest that targeted therapy choices should be considered in the context of race. [ABSTRACT FROM AUTHOR]

Published
2013
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32. Translating next generation sequencing to practice: Opportunities and necessary steps.

Author

Kamalakaran, Sitharthan, Varadan, Vinay, Janevski, Angel, Banerjee, Nilanjana, Tuck, David, McCombie, W. Richard, Dimitrova, Nevenka, and Harris, Lyndsay N.

Abstract

Next‐generation sequencing (NGS) approaches for measuring RNA and DNA benefit from greatly increased sensitivity, dynamic range and detection of novel transcripts. These technologies are rapidly becoming the standard for molecular assays and represent huge potential value to the practice of oncology. However, many challenges exist in the transition of these technologies from research application to clinical practice. This review discusses the value of NGS in detecting mutations, copy number changes and RNA quantification and their applications in oncology, the challenges for adoption and the relevant steps that are needed for translating this potential to routine practice. Highlights: Next Generation sequencing (NGS) enables measurement of clinically relevant mutations, DNA copy number and gene expression.We review diagnostic, prognostic and therapy selection applications of NGS for different types of cancer.We discuss technology challenges that need to be overcome for implementing NGS into widespread clinical use.We discuss education, regulatory framework, storage, privacy and confidentiality of genomic data to enable adoption. [ABSTRACT FROM AUTHOR]

Published
2013
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33. Modeling the clonal heterogeneity of stem cells.

Author

Tuck, David P. and Miranker, Willard

Subjects
STEM cells, CLONING, CELL proliferation, GENETIC engineering, TISSUES
Abstract

Recent experimental studies suggest that tissue stem cell pools are composed of functionally diverse clones. Metapopulation models in ecology concentrate on collections of populations and their role in stabilizing coexistence and maintaining selected genetic or epigenetic variation. Such models are characterized by expansion and extinction of spatially distributed populations. We develop a mathematical framework derived from the multispecies metapopulation model of Tilman et al (1994) to study the dynamics of heterogeneous stem cell metapopulations. In addition to normal stem cells, the model can be applied to cancer cell populations and their response to treatment. In our model disturbances may lead to expansion or contraction of cells with distinct properties, reflecting proliferation, apoptosis, and clonal competition. We first present closed-form expressions for the basic model which defines clonal dynamics in the presence of exogenous global disturbances. We then extend the model to include disturbances which are periodic and which may affect clones differently. Within the model framework, we propose a method to devise an optimal strategy of treatments to regulate expansion, contraction, or mutual maintenance of cells with specific properties. [ABSTRACT FROM AUTHOR]

Published
2010
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34. Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation.

Author

Emerson, John W., Dolled-Filhart, Marisa, Harris, Lyndsay, Rimm, David L., and Tuck, David P.

Subjects
BIOMARKERS, BREAST cancer diagnosis, IMMUNOHISTOCHEMISTRY, MULTIPLE imputation (Statistics), CANCER patients
Abstract

Missing data pose one of the greatest challenges in the rigorous evaluation of biomarkers. The limited availability of specimens with complete clinical annotation and quality biomaterial often leads to underpowered studies. Tissue microarray studies, for example, may be further handicapped by the loss of data points because of unevaluable staining, core loss, or the lack of tumor in the histospot. This paper presents a novel approach to these common problems in the context of a tissue protein biomarker analysis in a cohort of patients with breast cancer. Our analysis develops techniques based on multiple imputation to address the missing value problem. We first select markers using a training cohort, identifying a small subset of protein expression levels that are most useful in predicting patient survival. The best model is obtained by including both protein markers (including COX6C, GATA3, NAT1, and ESR1) and lymph node status. The use of either lymph node status or the four protein expression levels provides similar improvements in goodness-of-fit, with both significantly better than a baseline clinical model. Using the same multiple imputation strategy, we then validate the results out-of-sample on a larger independent cohort. Our approach of integrating multiple imputation with each stage of the analysis serves as an example that may be replicated or adapted in future studies with missing values. [ABSTRACT FROM AUTHOR]

Published
2009
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35. Differentiated cells are more efficient than adult stem cells for cloning by somatic cell nuclear transfer.

Author

Li-Ying Sung, Shaorong Gao, Hongmei Shen, Hui Yu, Yifang Song, Smith, Sadie L., Ching-Chien Chang, Inoue, Kimiko, Lynn Kuo, Jin Lian, Ao Li, Tian, X. Cindy, Tuck, David P., Weissman, Sherman M., Xiangzhong Yang, and Tao Cheng

Subjects
TRANSPLANTATION of cell nuclei, GENETIC engineering, SOMATIC cells, STEM cells, CLONING, HUMAN genetics
Abstract

Since the creation of Dolly via somatic cell nuclear transfer (SCNT), more than a dozen species of mammals have been cloned using this technology. One hypothesis for the limited success of cloning via SCNT (1%–5%) is that the clones are likely to be derived from adult stem cells. Support for this hypothesis comes from the findings that the reproductive cloning efficiency for embryonic stem cells is five to ten times higher than that for somatic cells as donors and that cloned pups cannot be produced directly from cloned embryos derived from differentiated B and T cells or neuronal cells. The question remains as to whether SCNT-derived animal clones can be derived from truly differentiated somatic cells. We tested this hypothesis with mouse hematopoietic cells at different differentiation stages: hematopoietic stem cells, progenitor cells and granulocytes. We found that cloning efficiency increases over the differentiation hierarchy, and terminally differentiated postmitotic granulocytes yield cloned pups with the greatest cloning efficiency. [ABSTRACT FROM AUTHOR]

Published
2006
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36. A semantic web approach to biological pathway data reasoning and integration.

Author

Cheung, Kei-Hoi, Qi, Peishen, Tuck, David, and Krauthammer, Michael

Subjects
SEMANTIC Web, ONTOLOGY, INFORMATION resources management, SEMANTIC networks (Information theory), SEMANTICS
Abstract

Abstract: This paper describes the use of semantic web technology and Description Logic (DL) for facilitating the integration of molecular pathway data, which is illustrated by an Web Ontology Language (OWL)-based transformation of a more complex pathway structure (Reactome) into a simpler one (HPRD). The process starts by adding OWL axioms to BioPAX, a pathway interchange standard. The axioms are designed for mapping BioPAX-formatted Reactome interactions to “molecular binding event” interactions, which can be easily aligned with the HPRD data. Using an automated OWL reasoner, we find overlapping and non-overlapping molecular interactions between the two pathway datasets. The paper demonstrates the potential of semantic web and its enabling technologies in biological pathway data reasoning and integration. [Copyright &y& Elsevier]

Published
2006
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37. Characterizing disease states from topological properties of transcriptional regulatory networks.

Author

Tuck, David P, Kluger, Harriet M, and Kluger, Yuval

Subjects
*GENE expression, *GENETIC transcription, *LEUKEMIA, *BREAST cancer, *TRANSCRIPTION factors
Abstract

Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. Results: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. Conclusion: Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states. [ABSTRACT FROM AUTHOR]

Published
2006
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40. The UK Supreme Court.

Author

Tuck, David

Subjects
CONSTITUTIONAL courts, COMMON law, ASSISTED suicide, PRENUPTIAL agreements, COURT rules
Published
2019

41. Leviathan.

Author

Tuck, David

Subjects
CHURCH & state, SOCIAL contract, WAR & society, AGRICULTURAL laborers, GREEK mythology
Published
2019

42. Feminism.

Author

Tuck, David

Subjects
FEMINISM, EQUALITY in the workplace, NUCLEAR families, NUCLEAR structure
Published
2020

43. Parliamentary privilege.

Author

Tuck, David

Subjects
POLITICAL privileges & immunities, BREXIT Referendum, 2016
Published
2017

44. The three strands of ecology.

Author

Tuck, David

Subjects
ECOLOGY, HUMAN behavior, DEEP ecology, HUMAN ecology, COMMONS, SOCIAL ecology
Published
2019

45. Reply to “On the cloning of animals from terminally differentiated cells”.

Author

Xiangzhong Yang, Tao Cheng, Li-Ying Sung, Shaorong Gao, Hongmei Shen, Hui Yu, Yifang Song, Smith, Sadie L., Tuck, David P., Inoue, Kimiko, and Weissman, Sherman M.

Subjects
LETTERS to the editor, CLONING
Abstract

A response by Yang to a letter to the editor about their article "Differentiated cells are more efficient than adult stem cells for cloning by somatic cell nuclear transfer," is presented.

Published
2007
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46. You saucy things...

Author

Watson, Ian and Tuck, David

Subjects
COMEDIANS
Abstract

Features the comedy team Lee and Herring. Performance in the comedy show titled `Top of the Pops'; Comedy style; Career plans.

Published
1995

47. Unfringed!

Author

Watson, Ian and Tuck, David

Subjects
ART festivals
Abstract

Reports on the 1995 Edinburgh Festival Fringe. Anxiety among music performers; Reputation of music performers; Rumors; Outcome of the meetings of the Perrier Award Panel.

Published
1995

48. Sean again.

Author

Tuck, David and Bonner, Michael

Subjects
TELEVISION programs
Abstract

Reviews the television program `Thirtysomehow,' which features actor Sean Hughes. Criticism of Hughes performance.

Published
1995

49. Distinct human papillomavirus type 16 methylomes in cervical cells at different stages of premalignancy

Author

Brandsma, Janet L., Sun, Ying, Lizardi, Paul M., Tuck, David P., Zelterman, Daniel, Haines, G. Kenneth, Martel, Maritza, Harigopal, Malini, Schofield, Kevin, and Neapolitano, Matthew

Subjects
*VIRAL genomes, *PAPILLOMAVIRUSES, *CARCINOGENESIS, *PRECANCEROUS conditions, *GENETIC regulation, *METHYLATION, *DNA, *HUMAN gene mapping, *GENETICS
Abstract

Abstract: Human papillomavirus (HPV) gene expression is dramatically altered during cervical carcinogenesis. Because dysregulated genes frequently show abnormal patterns of DNA methylation, we hypothesized that comprehensive mapping of the HPV methylomes in cervical samples at different stages of progression would reveal patterns of clinical significance. To test this hypothesis, thirteen HPV16-positive samples were obtained from women undergoing routine cervical cancer screening. Complete methylation data were obtained for 98.7% of the HPV16 CpGs in all samples by bisulfite-sequencing. Most HPV16 CpGs were unmethylated or methylated in only one sample. The other CpGs were methylated at levels ranging from 11% to 100% of the HPV16 copies per sample. The results showed three major patterns and two variants of one pattern. The patterns showed minimal or no methylation (A), low level methylation in the E1 and E6 genes (B), and high level methylation at many CpGs in the E5/L2/L1 region (C). Generally, pattern A was associated with negative cytology, pattern B with low-grade lesions, and pattern C with high-grade lesions. The severity of the cervical lesions was then ranked by the HPV16 DNA methylation patterns and, independently, by the pathologic diagnoses. Statistical analysis of the two rating methods showed highly significant agreement. In conclusion, analysis of the HPV16 DNA methylomes in clinical samples of cervical cells led to the identification of distinct methylation patterns which, after validation in larger studies, could have potential utility as biomarkers of neoplastic cervical progression. [Copyright &y& Elsevier]

Published
2009
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50. Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

Author

Lin E, Zwolinski R, Wu JT, La J, Goryachev S, Huhmann L, Yildrim C, Tuck DP, Elbers DC, Brophy MT, Do NV, and Fillmore NR

Subjects
Humans, Medical Records, Software, Machine Learning, Electronic Health Records, Natural Language Processing, B7-H1 Antigen
Abstract

Introduction: PD-L1 expression is used to determine oncology patients' response to and eligibility for immunologic treatments; however, PD-L1 expression status often only exists in unstructured clinical notes, limiting ability to use it in population-level studies. Methods: We developed and evaluated a machine learning based natural language processing (NLP) tool to extract PD-L1 expression values from the nationwide Veterans Affairs electronic health record system. Results: The model demonstrated strong evaluation performance across multiple levels of label granularity. Mean precision of the overall PD-L1 positive label was 0.859 (sd, 0.039), recall 0.994 (sd, 0.013), and F1 0.921 (0.024). When a numeric PD-L1 value was identified, the mean absolute error of the value was 0.537 on a scale of 0 to 100. Conclusion: We presented an accurate NLP method for deriving PD-L1 status from clinical notes. By reducing the time and manual effort needed to review medical records, our work will enable future population-level studies in cancer immunotherapy.

Published
2023
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