11 results on '"Wang, Shuaicheng"'
Search Results
2. Correction: Biomarkers and overall survival in patients with advanced hepatocellular carcinoma treated with TGF-βRI inhibitor galunisertib.
- Author
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Giannelli, Gianluigi, Santoro, Armando, Kelley, Robin, Gane, Ed, Paradis, Valerie, Cleverly, Ann, Smith, Claire, Estrem, Shawn, Man, Michael, Wang, Shuaicheng, Lahn, Michael, Raymond, Eric, Benhadji, Karim, and Faivre, Sandrine
- Abstract
[This corrects the article DOI: 10.1371/journal.pone.0222259.].
- Published
- 2021
3. Biomarkers and overall survival in patients with advanced hepatocellular carcinoma treated with TGF-βRI inhibitor galunisertib
- Author
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Giannelli, Gianluigi, Santoro, Armando, Kelley, Robin K, Gane, Ed, Paradis, Valerie, Cleverly, Ann, Smith, Claire, Estrem, Shawn T, Man, Michael, Wang, Shuaicheng, Lahn, Michael M, Raymond, Eric, Benhadji, Karim A, and Faivre, Sandrine
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Rare Diseases ,Clinical Research ,Digestive Diseases ,Liver Cancer ,Liver Disease ,Cancer ,Evaluation of treatments and therapeutic interventions ,6.1 Pharmaceuticals ,Adult ,Aged ,Aged ,80 and over ,Antigens ,CD ,Biomarkers ,Tumor ,Cadherins ,Carcinoma ,Hepatocellular ,Cohort Studies ,Female ,Humans ,Liver Neoplasms ,Male ,MicroRNAs ,Middle Aged ,Prognosis ,Pyrazoles ,Quinolines ,Receptor ,Transforming Growth Factor-beta Type I ,Survival Rate ,Transforming Growth Factor beta1 ,Treatment Outcome ,alpha-Fetoproteins ,General Science & Technology - Abstract
BackgroundTransforming growth factor beta (TGF-β) signalling is involved in the development of hepatocellular carcinoma (HCC). We followed changes in biomarkers during treatment of patients with HCC with the TGF-βRI/ALK5 inhibitor galunisertib.MethodsThis phase 2 study (NCT01246986) enrolled second-line patients with advanced HCC into one of two cohorts of baseline serum alpha-fetoprotein (AFP): Part A (AFP ≥1.5x ULN) or Part B (AFP 20% from baseline) versus non-responders, median OS was 21.5 months versus 6.8 months (p = 0.0015). In TGF-β1 responders (51% of all patients) versus non-responders, median OS was 11.2 months versus 5.3 months (p = 0.0036).ConclusionsConsistent with previous findings, both baseline levels and changes from baseline of circulating AFP and TGF-β1 function as prognostic indicators of survival. Future trials are needed to confirm and extend these results.
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- 2020
4. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials
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't Hart, L.M., Abdalla, M., Adam, J., Adamski, J., Adragni, K., Allin, K.H., Arumugam, M., Atabaki Pasdar, N., Baltauss, T., Banasik, K.B., Baum, P., Bell, J.D., Bergstrom, M., Beulens, J.W., Bianzano, S., Bizzotto, R., Bonneford, A., Brorsson, C.A.B., Brown, A.A., Brunak, S.B., Cabrelli, L., Caiazzo, R., Canouil, M., Dale, M., Davtian, D., Dawed, A.Y., De Masi, F.M., de Preville, N., Dekkers, K.F., Dermitzakis, E.T., Deshmukh, H.A., Dings, C., Donnelly, L., Dutta, A., Ehrhardt, B., Elders, P.J.M., Engel Thomas, C.E.T., Engelbrechtsen, L., Eriksen, R.G., Eriksen, R.E., Fan, Y., Fernandez, J., Ferrer, J., Fitipaldi, H., Forgie, I.M., Forman, A., Franks, P.W., Frau, F., Fritsche, A., Froguel, P., Frost, G., Gassenhuber, J., Giordano, G.N., Giorgino, T., Gough, S., Graefe-Mody, U., Grallert, H., Grempler, R., Groeneveld, L., Groop, L., Gudmundsdóttir, V.G., Gupta, R.G., Haid, M., Hansen, T., Hansen, T.H., Hattersley, A.T., Haussler, R.S., Heggie, A.J., Hennige, A.M., Hill, A.V., Holl, R.W., Hong, M.-G., Hudson, M., Jablonka, B., Jennison, C., Jiao, J., Johansen, J.J., Jones, A.G., Jonsson, A., Karaderi, T.K., Kaye, J., Klintenberg, M., Koivula, R.W., Kokkola, T., Koopman, A.D.M., Kurbasic, A, Kuulasmaa, T., Laakso, M., Lehr, T., Loftus, H., Lundbye Allesøe, R.L.A, Mahajan, A., Mari, A., Mazzoni, G.M., McCarthy, M.I., McDonald, T.J., McEvoy, D., McRobert, N., McVittie, I., Mourby, M., Musholt, P., Mutie, P, Nice, R., Nicolay, C., Nielsen, A.M.N., Nilsson, B.N., Palmer, C.N., Pattou, F., Pavo, I., Pearson, E.R., Pedersen, O., Pedersen, H.K.P., Perry, M.H., Pomares-Millan, H., Ramisch, A., Rasmussen, S.R., Raverdi, V., Ridderstrale, M., Robertson, N., Roderick, R.C., Rodriquez, M., Ruetten, H., Rutters, F., Sackett, W., Scherer, N., Schwenk, J.M., Shah, N., Sharma, S., Sihinevich, I., Sondertoft, N.B., Staerfeldt, H., Steckel-Hamann, B., Teare, H., Thomas, M.K., Thomas, E.L., Thomsen, H.S., Thorand, B., Thorne, C.E., Tillner, J., Troen Lundgaard, A.T.L., Troll, M., Tsirigos, K.D.T., Tura, A., Uhlen, M., van Leeuwen, N., van Oort, S., Verkindt, H., Vestergaard, H., Viñuela, A., Vogt, J.K, Wad Sackett, P.W.S, Wake, D., Walker, M., Wesolowska-Andersen, A., Whitcher, B., White, M.W., Wu, H., Dawed, Adem Y, Mari, Andrea, Brown, Andrew, McDonald, Timothy J, Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R, Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M ‘t, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G, and Pearson, Ewan R
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- 2023
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5. Phase 1b/2a study of galunisertib, a small molecule inhibitor of transforming growth factor-beta receptor I, in combination with standard temozolomide-based radiochemotherapy in patients with newly diagnosed malignant glioma
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Wick, Antje, Desjardins, Annick, Suarez, Cristina, Forsyth, Peter, Gueorguieva, Ivelina, Burkholder, Tiana, Cleverly, Ann Louise, Estrem, Shawn T., Wang, Shuaicheng, Lahn, Michael M., Guba, Susan C., Capper, David, and Rodon, Jordi
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- 2020
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6. TGFβ receptor inhibitor galunisertib is linked to inflammation- and remodeling-related proteins in patients with pancreatic cancer
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Melisi, Davide, Garcia-Carbonero, Rocio, Macarulla, Teresa, Pezet, Denis, Deplanque, Gael, Fuchs, Martin, Trojan, Jorg, Kozloff, Mark, Simionato, Francesca, Cleverly, Ann, Smith, Claire, Wang, Shuaicheng, Man, Michael, Driscoll, Kyla E., Estrem, Shawn T., Lahn, Michael M. F., Benhadji, Karim A., and Tabernero, Josep
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- 2019
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7. Pharmacogenomics of GLP-1 receptor agonists : a genome-wide analysis of observational data and large randomised controlled trials
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Dawed, Adem Y., Mari, Andrea, Brown, Andrew, McDonald, Timothy J., Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R., Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M. 't, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G., Pearson, Ewan R., DIRECT consortium, for the D. I. R. E. C. T. consortium, Dawed, Adem Y., Mari, Andrea, Brown, Andrew, McDonald, Timothy J., Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R., Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M. 't, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G., Pearson, Ewan R., and DIRECT consortium, for the D. I. R. E. C. T. consortium
- Abstract
Background: In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment. Methods: In this genome-wide analysis we included adults (aged >= 18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests. Findings: 4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G -> A (Gly168Ser) in the GLP1R (0.08% [95% CI 0.04-0.12] or 0.9 mmol/mol lower reduction in HbA1c per serine, p=6.0 x 10(-5)) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6.7 x 10(-8)), largely driven by rs140226575G -> A (Thr370Met; 0.25% [SE 0.06] or 2.7 mmol/mol [SE 0.7] greater HbA1c reduction per methionine, p=5.2 x 10(-6)). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes i
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- 2023
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8. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials
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Dawed, Adem Y, primary, Mari, Andrea, additional, Brown, Andrew, additional, McDonald, Timothy J, additional, Li, Lin, additional, Wang, Shuaicheng, additional, Hong, Mun-Gwan, additional, Sharma, Sapna, additional, Robertson, Neil R, additional, Mahajan, Anubha, additional, Wang, Xuan, additional, Walker, Mark, additional, Gough, Stephen, additional, Hart, Leen M ‘t, additional, Zhou, Kaixin, additional, Forgie, Ian, additional, Ruetten, Hartmut, additional, Pavo, Imre, additional, Bhatnagar, Pallav, additional, Jones, Angus G, additional, Pearson, Ewan R, additional, 't Hart, L.M., additional, Abdalla, M., additional, Adam, J., additional, Adamski, J., additional, Adragni, K., additional, Allin, K.H., additional, Arumugam, M., additional, Atabaki Pasdar, N., additional, Baltauss, T., additional, Banasik, K.B., additional, Baum, P., additional, Bell, J.D., additional, Bergstrom, M., additional, Beulens, J.W., additional, Bianzano, S., additional, Bizzotto, R., additional, Bonneford, A., additional, Brorsson, C.A.B., additional, Brown, A.A., additional, Brunak, S.B., additional, Cabrelli, L., additional, Caiazzo, R., additional, Canouil, M., additional, Dale, M., additional, Davtian, D., additional, Dawed, A.Y., additional, De Masi, F.M., additional, de Preville, N., additional, Dekkers, K.F., additional, Dermitzakis, E.T., additional, Deshmukh, H.A., additional, Dings, C., additional, Donnelly, L., additional, Dutta, A., additional, Ehrhardt, B., additional, Elders, P.J.M., additional, Engel Thomas, C.E.T., additional, Engelbrechtsen, L., additional, Eriksen, R.G., additional, Eriksen, R.E., additional, Fan, Y., additional, Fernandez, J., additional, Ferrer, J., additional, Fitipaldi, H., additional, Forgie, I.M., additional, Forman, A., additional, Franks, P.W., additional, Frau, F., additional, Fritsche, A., additional, Froguel, P., additional, Frost, G., additional, Gassenhuber, J., additional, Giordano, G.N., additional, Giorgino, T., additional, Gough, S., additional, Graefe-Mody, U., additional, Grallert, H., additional, Grempler, R., additional, Groeneveld, L., additional, Groop, L., additional, Gudmundsdóttir, V.G., additional, Gupta, R.G., additional, Haid, M., additional, Hansen, T., additional, Hansen, T.H., additional, Hattersley, A.T., additional, Haussler, R.S., additional, Heggie, A.J., additional, Hennige, A.M., additional, Hill, A.V., additional, Holl, R.W., additional, Hong, M.-G., additional, Hudson, M., additional, Jablonka, B., additional, Jennison, C., additional, Jiao, J., additional, Johansen, J.J., additional, Jones, A.G., additional, Jonsson, A., additional, Karaderi, T.K., additional, Kaye, J., additional, Klintenberg, M., additional, Koivula, R.W., additional, Kokkola, T., additional, Koopman, A.D.M., additional, Kurbasic, A, additional, Kuulasmaa, T., additional, Laakso, M., additional, Lehr, T., additional, Loftus, H., additional, Lundbye Allesøe, R.L.A, additional, Mahajan, A., additional, Mari, A., additional, Mazzoni, G.M., additional, McCarthy, M.I., additional, McDonald, T.J., additional, McEvoy, D., additional, McRobert, N., additional, McVittie, I., additional, Mourby, M., additional, Musholt, P., additional, Mutie, P, additional, Nice, R., additional, Nicolay, C., additional, Nielsen, A.M.N., additional, Nilsson, B.N., additional, Palmer, C.N., additional, Pattou, F., additional, Pavo, I., additional, Pearson, E.R., additional, Pedersen, O., additional, Pedersen, H.K.P., additional, Perry, M.H., additional, Pomares-Millan, H., additional, Ramisch, A., additional, Rasmussen, S.R., additional, Raverdi, V., additional, Ridderstrale, M., additional, Robertson, N., additional, Roderick, R.C., additional, Rodriquez, M., additional, Ruetten, H., additional, Rutters, F., additional, Sackett, W., additional, Scherer, N., additional, Schwenk, J.M., additional, Shah, N., additional, Sharma, S., additional, Sihinevich, I., additional, Sondertoft, N.B., additional, Staerfeldt, H., additional, Steckel-Hamann, B., additional, Teare, H., additional, Thomas, M.K., additional, Thomas, E.L., additional, Thomsen, H.S., additional, Thorand, B., additional, Thorne, C.E., additional, Tillner, J., additional, Troen Lundgaard, A.T.L., additional, Troll, M., additional, Tsirigos, K.D.T., additional, Tura, A., additional, Uhlen, M., additional, van Leeuwen, N., additional, van Oort, S., additional, Verkindt, H., additional, Vestergaard, H., additional, Viñuela, A., additional, Vogt, J.K, additional, Wad Sackett, P.W.S, additional, Wake, D., additional, Walker, M., additional, Wesolowska-Andersen, A., additional, Whitcher, B., additional, White, M.W., additional, and Wu, H., additional
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- 2023
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9. Analytical Characterization of an Enzyme-Linked Immunosorbent Assay for the Measurement of Transforming Growth Factor β1 in Human Plasma
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Giles, Brendan M, primary, Underwood, Timothy T, additional, Benhadji, Karim A, additional, Nelson, Diana K S, additional, Grobeck, Lisa M, additional, Lin, Boris, additional, Wang, Shuaicheng, additional, Fill, Jeffrey A, additional, Man, Michael, additional, Pitts, Kelly R, additional, and Bamberg, Alison, additional
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- 2018
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10. Subgroups from regression trees with adjustment for prognostic effects and postselection inference
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Loh, Wei-Yin, primary, Man, Michael, additional, and Wang, Shuaicheng, additional
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- 2018
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11. Subgroups from regression trees with adjustment for prognostic effects and postselection inference.
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Loh, Wei-Yin, Man, Michael, and Wang, Shuaicheng
- Abstract
Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal-theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored. [ABSTRACT FROM AUTHOR]
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- 2019
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