Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

Abstract While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients...

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Autores principales:	Angela M. Jarrett, David A. Hormuth, Vikram Adhikarla, Prativa Sahoo, Daniel Abler, Lusine Tumyan, Daniel Schmolze, Joanne Mortimer, Russell C. Rockne, Thomas E. Yankeelov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b0d9d0ed140743fbab8a82a362ac28df
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spelling	oai:doaj.org-article:b0d9d0ed140743fbab8a82a362ac28df2021-12-02T11:41:18ZTowards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer10.1038/s41598-020-77397-02045-2322https://doaj.org/article/b0d9d0ed140743fbab8a82a362ac28df2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77397-0https://doaj.org/toc/2045-2322Abstract While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.Angela M. JarrettDavid A. HormuthVikram AdhikarlaPrativa SahooDaniel AblerLusine TumyanDaniel SchmolzeJoanne MortimerRussell C. RockneThomas E. YankeelovNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Angela M. Jarrett David A. Hormuth Vikram Adhikarla Prativa Sahoo Daniel Abler Lusine Tumyan Daniel Schmolze Joanne Mortimer Russell C. Rockne Thomas E. Yankeelov Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
description	Abstract While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.
format	article
author	Angela M. Jarrett David A. Hormuth Vikram Adhikarla Prativa Sahoo Daniel Abler Lusine Tumyan Daniel Schmolze Joanne Mortimer Russell C. Rockne Thomas E. Yankeelov
author_facet	Angela M. Jarrett David A. Hormuth Vikram Adhikarla Prativa Sahoo Daniel Abler Lusine Tumyan Daniel Schmolze Joanne Mortimer Russell C. Rockne Thomas E. Yankeelov
author_sort	Angela M. Jarrett
title	Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
title_short	Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
title_full	Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
title_fullStr	Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
title_full_unstemmed	Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
title_sort	towards integration of 64cu-dota-trastuzumab pet-ct and mri with mathematical modeling to predict response to neoadjuvant therapy in her2 + breast cancer
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b0d9d0ed140743fbab8a82a362ac28df
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Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

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