Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Ryan D. Milligan"'
Autor:
Balraj Singh, Vanessa N. Sarli, Ryan D. Milligan, Hannah E. Kinne, Anna Shamsnia, Laura J. Washburn, Sridevi Addanki, Anthony Lucci
Publikováno v:
Cancers, Vol 15, Iss 7, p 2036 (2023)
We treated highly metabolically adaptable (SUM149-MA) triple-negative inflammatory breast cancer cells and their control parental SUM149-Luc cell line with JQ1 for long periods to determine its efficacy at inhibiting therapy-resistant cells. After 20
Externí odkaz:
https://doaj.org/article/bb40f63797b14738810bcb64ec44d9bd
Autor:
Balraj Singh, Vanessa N. Sarli, Ryan D. Milligan, Hannah E. Kinne, Anna Shamsnia, Laura J. Washburn, Anthony Lucci
Background: Cell culture models of cancer typically favor proliferative but therapy-sensitive cells because body-like selection pressures are absent. To address this limitation of cell culture, we previously described a function-based selection strat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d667d3be71bc9a2dea4ceda9b401e31
https://doi.org/10.21203/rs.3.rs-1832724/v1
https://doi.org/10.21203/rs.3.rs-1832724/v1
Publikováno v:
PLoS ONE, Vol 11, Iss 7, p e0159072 (2016)
We have previously shown that only 0.01% cells survive a metabolic challenge involving lack of glutamine in culture medium of SUM149 triple-negative Inflammatory Breast Cancer cell line. These cells, designated as SUM149-MA for metabolic adaptability
Externí odkaz:
https://doaj.org/article/c884f3f26bd64bbebf45832ac7c2c275
Autor:
Balraj Singh, Anna Shamsnia, Milan R Raythatha, Ryan D Milligan, Amanda M Cady, Simran Madan, Anthony Lucci
Publikováno v:
PLoS ONE, Vol 9, Iss 10, p e109487 (2014)
A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling hi
Externí odkaz:
https://doaj.org/article/f29eee1a74d94afcbfc5bbfad42b92a5
Publikováno v:
Cancer Research. 77:P3-07
We have recently developed a usable model of panresistance in triple-negative breast cancer. We have shown that only 0.01% cells survive a metabolic challenge involving lack of glutamine in culture medium of SUM149 triple-negative Inflammatory Breast
Publikováno v:
Day 1 Tue, November 27, 2018.
Post yield design methodology using Ductile Failure Damage Indicator (DFDI) for well tubulars was proposed and has been used for tubulars and connections life assessment. The tubular design assessment model incorporates a connection strain localizati
Autor:
Laura J. Washburn, Balraj Singh, Hannah E. Kinne, Mark Olsen, Ryan D. Milligan, Anthony Lucci
Publikováno v:
PLoS ONE
PLoS ONE, Vol 11, Iss 7, p e0159072 (2016)
PLoS ONE, Vol 11, Iss 7, p e0159072 (2016)
We have previously shown that only 0.01% cells survive a metabolic challenge involving lack of glutamine in culture medium of SUM149 triple-negative Inflammatory Breast Cancer cell line. These cells, designated as SUM149-MA for metabolic adaptability
Autor:
Mark Olsen, Balraj Singh, Ryan D. Milligan, Hannah E. Kinne, Anthony Lucci, Laura J. Washburn
Publikováno v:
Cancer Research. 76:2416-2416
Cancer is an evolution-like process. Effectively dealing with the currently untreatable component of breast cancer (the cells that drive metastasis) is vital. In this regard, we have developed a model of panresistant cancer cells that allows us to ev
Autor:
Ryan D. Milligan, Amanda M. Cady, Anthony Lucci, Balraj Singh, Anna Shamsnia, Simran Madan, Milan R. Raythatha
Publikováno v:
PLoS ONE, Vol 9, Iss 10, p e109487 (2014)
PLoS ONE
PLoS ONE
A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling hi
Publikováno v:
Cancer Research. 75:1719-1719
A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We propose a strategy for optimally modeling hig