Abstract P6-17-33: A multi-scale mathematical model of combination targeted and cytotoxic therapy to evaluate treatment response in HER2+ breast cancer

2019 ◽  
Author(s):  
AM Jarrett ◽  
A Shah ◽  
MJ Bloom ◽  
TE Yankeelov ◽  
AG Sorace
Keyword(s):  
Breast Cancer ◽  
Mathematical Model ◽  
Treatment Response ◽  
Cytotoxic Therapy ◽  
Multi Scale ◽  
Her2 Breast Cancer

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Angela M. Jarrett ◽  
David A. Hormuth ◽  
Vikram Adhikarla ◽  
Prativa Sahoo ◽  
Daniel Abler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Targeted Therapy ◽  
Treatment Response ◽  
Growth Factor Receptor ◽  
Patient Specific ◽  
Mathematical Framework ◽  
Breast Cancer Patients ◽  
Data Types ◽  
Her2 Breast Cancer ◽  
Mri Scans

AbstractWhile 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.

scholarly journals Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer

2018 ◽  
Vol 36 (3) ◽  
pp. 381-410 ◽  
Author(s):  
Angela M Jarrett ◽  
Meghan J Bloom ◽  
Wesley Godfrey ◽  
Anum K Syed ◽  
David A Ekrut ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Experimental Data ◽  
Mathematical Model ◽  
Immune Response ◽  
Immune System ◽  
Murine Model ◽  
Tumour Growth ◽  
Experimental Approach ◽  
Her2 Breast Cancer ◽  
The Mathematical Model

Abstract The goal of this study is to develop an integrated, mathematical–experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls ($p < 0.03$). We posit that the proposed mathematical–experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth—conclusions revealed by the mathematical model that were not deducible from the experimental data alone.

scholarly journals Deep learning trained on H&E tumor ROIs predicts HER2 status and Trastuzumab treatment response in HER2+ breast cancer

2021 ◽  
Author(s):  
saman farahmand ◽  
Aileen Fernandez ◽  
Fahad Shabbir Ahmed ◽  
David Rimm ◽  
Jeffrey H Chuang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Response ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Standard Of Care ◽  
Her2 Status ◽  
Current Standard ◽  
Her2 Positive ◽  
Trastuzumab Therapy ◽  
Her2 Breast Cancer

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that is better than IHC and may benefit clinical evaluations.

scholarly journals Trastuzumab improves tumor perfusion and vascular delivery of cytotoxic therapy in a murine model of HER2+ breast cancer: preliminary results

2016 ◽  
Vol 155 (2) ◽  
pp. 273-284 ◽  
Author(s):  
Anna G. Sorace ◽  
C. Chad Quarles ◽  
Jennifer G. Whisenant ◽  
Ariella B. Hanker ◽  
J. Oliver McIntyre ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Murine Model ◽  
Tumor Perfusion ◽  
Cytotoxic Therapy ◽  
Preliminary Results ◽  
Her2 Breast Cancer

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

2020 ◽  
Author(s):  
Angela M. Jarrett ◽  
David A. Hormuth ◽  
Vikram Adhikarla ◽  
Prativa Sahoo ◽  
Daniel Abler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Targeted Therapy ◽  
Treatment Response ◽  
Growth Factor Receptor ◽  
Patient Specific ◽  
Mathematical Framework ◽  
Breast Cancer Patients ◽  
Data Types ◽  
Her2 Breast Cancer ◽  
Mri Scans

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.

scholarly journals Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Angela M. Jarrett ◽  
Alay Shah ◽  
Meghan J. Bloom ◽  
Matthew T. McKenna ◽  
David A. Hormuth ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mathematical Modeling ◽  
Cytotoxic Therapy ◽  
Her2 Breast Cancer

E2F cell cycle pathway score as a predictive biomarker of ER+/HER2- breast cancer response to neoadjuvant chemotherapy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12593-e12593
Author(s):  
Masanori Oshi ◽  
Yoshihisa Tokumaru ◽  
Eriko Katsuta ◽  
Li Yan ◽  
Itaru Endo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Predictive Biomarker ◽  
Related Factor ◽  
Breast Cancers ◽  
Her2 Breast Cancer ◽  
Cell Cycle Pathway ◽  
Response To Neoadjuvant Chemotherapy

e12593 Background: Although the survival rate of patients diagnosed with breast cancer has greatly improved over the last decades, the metastatic stage of the disease remains incurable. Estrogen receptor positive (ER+)/human receptor 2 negative (HER2-) breast cancers accounts for approximately 70% of metastatic breast cancers and, it is responsible for most of the deaths from the disease. Another issue of ER+/HER2- breast cancer is its poor response to neoadjuvant chemotherapy (NAC). Recently, several clinical trials have shown that endocrine therapy plus FDA-approved cyclin-dependent kinases (CDK) 4/6 inhibitors have improved the treatment response to patients with advanced ER+/HER2 breast. The CDK 4/6 is an important E2F-related factor. Therefore, we hypothesized that E2F pathway is predictive biomarker for treatment response of ER+/HER2- breast cancer. Methods: METABRIC dataset was used for training cohort. And TCGA breast cancer as well as four neoadjuvant therapeutic cohorts were used as validation cohort. The E2F pathway score was calculated by Gene Set Variant Analysis (GSVA). Results: Tumors with high E2F pathway score enriched cell proliferation and cell cycle pathway related gene sets (G2M, MYC v1, and v2, MITOTIC, MTORC1, PI3K AKT MTOR, and DNA repair; FDR < 0.001). The high E2F pathway score was significantly associated with TNBC and HER2+ (p < 0.001), which are known to be clinically aggressive, and advanced stage (p < 0.001) and high grade (p < 0.001). The score also correlated with copy mutation (r = 0.605, p < 0.01). The patients, who had early recurrence before 5 years, had significantly high score rather than non-recurrence patients (p < 0.001). Moreover, these results were validated in TCGA cohort. The high E2F pathway score group had a higher pCR rate than the low score group with ER+/HER2- breast cancer (p < 0.001). Interestingly, the tumors in high score group expressed higher CDK pathway related molecules compared to low score group. Conclusions: The E2F pathway score was found to be a predictive biomarker for neoadjuvant chemotherapy in ER+/HER2- breast cancer patients.

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