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...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2020-11-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-020-77397-0 |
id |
doaj-b0d9d0ed140743fbab8a82a362ac28df |
---|---|
record_format |
Article |
spelling |
doaj-b0d9d0ed140743fbab8a82a362ac28df2020-12-08T10:49:04ZengNature Publishing GroupScientific Reports2045-23222020-11-0110111410.1038/s41598-020-77397-0Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancerAngela M. Jarrett0David A. Hormuth1Vikram Adhikarla2Prativa Sahoo3Daniel Abler4Lusine Tumyan5Daniel Schmolze6Joanne Mortimer7Russell C. Rockne8Thomas E. Yankeelov9Oden Institute for Computational Engineering and Sciences, The University of Texas At AustinOden Institute for Computational Engineering and Sciences, The University of Texas At AustinDivision of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical CenterDivision of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical CenterDivision of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical CenterDepartment of Radiology, City of Hope National Medical CenterDepartment of Pathology, City of Hope National Medical CenterDepartment of Medical Oncology & Therapeutics Research, City of Hope National Medical CenterDivision of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical CenterOden Institute for Computational Engineering and Sciences, The University of Texas At AustinAbstract 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.https://doi.org/10.1038/s41598-020-77397-0 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
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 |
spellingShingle |
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 Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2020-11-01 |
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. |
url |
https://doi.org/10.1038/s41598-020-77397-0 |
work_keys_str_mv |
AT angelamjarrett towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT davidahormuth towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT vikramadhikarla towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT prativasahoo towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT danielabler towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT lusinetumyan towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT danielschmolze towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT joannemortimer towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT russellcrockne towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer AT thomaseyankeelov towardsintegrationof64cudotatrastuzumabpetctandmriwithmathematicalmodelingtopredictresponsetoneoadjuvanttherapyinher2breastcancer |
_version_ |
1724389778751750144 |