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Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer
by
Wicker, Matthew
, Damaghi, Mehdi
, Strobl, Maximilian
, Koppens, Martijn
, Pooladvand, Pantea
, Adhikarla, Vikram
, Lakatos, Eszter
, Gatenby, Chandler
, Saputro, Linggih
, Wenham, Robert
, Gallaher, Jill A
, Schenck, Ryan
, Shockey, Andrew
, Salvador Cruz Garcia
in
Cancer Biology
/ Evolution & development
/ Mathematical models
/ Metastases
/ Metastasis
/ Ovarian cancer
/ Tumor cells
2018
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Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer
by
Wicker, Matthew
, Damaghi, Mehdi
, Strobl, Maximilian
, Koppens, Martijn
, Pooladvand, Pantea
, Adhikarla, Vikram
, Lakatos, Eszter
, Gatenby, Chandler
, Saputro, Linggih
, Wenham, Robert
, Gallaher, Jill A
, Schenck, Ryan
, Shockey, Andrew
, Salvador Cruz Garcia
in
Cancer Biology
/ Evolution & development
/ Mathematical models
/ Metastases
/ Metastasis
/ Ovarian cancer
/ Tumor cells
2018
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Do you wish to request the book?
Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer
by
Wicker, Matthew
, Damaghi, Mehdi
, Strobl, Maximilian
, Koppens, Martijn
, Pooladvand, Pantea
, Adhikarla, Vikram
, Lakatos, Eszter
, Gatenby, Chandler
, Saputro, Linggih
, Wenham, Robert
, Gallaher, Jill A
, Schenck, Ryan
, Shockey, Andrew
, Salvador Cruz Garcia
in
Cancer Biology
/ Evolution & development
/ Mathematical models
/ Metastases
/ Metastasis
/ Ovarian cancer
/ Tumor cells
2018
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Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer
Paper
Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer
2018
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Overview
Ovarian cancer has the highest mortality rate of all gynecologic cancers, which may be attributed to an often late stage diagnosis, when the cancer is already metastatic, and rapid development of treatment resistance. We propose that the metastatic disease could be better characterized by observing interactions within the microenvironmental niche of the primary site that shapes the tumor's early phenotypic progression. We present a mechanistic mathematical model of ovarian cancer that considers spatial interactions between tumor cells and several key stromal components. We demonstrate how spatial biomarker imaging data from the primary tumor can be analyzed to define a patient-specific microenvironment in the mathematical model. We then show preliminary results, using this model, that demonstrate how differences in the niche composition of a tumor affects phenotypic evolution and treatment response.
Publisher
Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
Subject
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