Towards Precision Oncology in Breast Cancer

Predicting Response to Neoadjuvant Chemotherapy Using Tumor Vasculature Characteristics

Ursa Brown-Glaberman, MD – University of New Mexico Comprehensive Cancer Center, Albuquerque, NM 87131, USA

Terisse A. Brocato, PhD – Department of Chemical and Biological Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA

Reed Selwyn, PhD – Department of Radiology, University of New Mexico, Albuquerque, NM 87131, USA

Zhihui Wang, PhD – Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA

Vittorio Cristini, PhD – Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA

Breast cancer is a model disease in precision oncology, leading the way in the development of both targeted therapy and prognostic and predictive biomarkers. However, predictive biomarker based treatment selection remains an elusive goal in the management of many women with this disease. Our group has developed a modeling tool to predict the likelihood of response to neo­adjuvant chemotherapy using patient specific tumor vasculature biomarkers. Here, we describe a novel integrated study based on a mathematical model utilizing tumor vasculature characteristics paired with patient data analysis to predict response to neoadjuvant chemotherapy in women with high risk hormone receptor positive, HER2 negative early stage breast cancer.

Fig. 1. Diffusion analysis workflow. A) Shows the original CD34 (tumor vasculature) stained histology grid before any processing. B) Displays the same tissue region as in A, but with the outer inked portion removed due to the increased likelihood of false positives on the perimeter of core biopsy samples. C) Demonstrates a computerized version of B and differentiates between tissue CD34- (blue), vasculature CD34+ (red), and non-tissue regions (grey). D) Shows the diffusion analysis of image C, which was performed by code developed in Matlab. Parameters measured are: vessel radius (rb), blood volume fraction (BVF), and diffusion distance (L). Vessels are outlined in red, and total area of blood vessels in a tissue region is blood volume fraction, BVF. Radius of blood vessels which are measured at each blue point inside of a vessel (outlined in red). An average of all vessel radii in each image analyzed is taken to be rb (µm). The farthest distance nutrients or drug need to travel from a vessel to reach all tissue, the distance from that point to vessel in red is measured at each point in black, all distances averaged is the diffusion penetration distance, L, measured in µm. White is the tumor tissue region, all of which is considered for analysis. Green is the background/non-tissue region not considered for analysis.

We have pioneered a semi-automated analysis method that allows for increased measurement accuracy and rapid throughput in rendering model predictions, with hundreds images analyzed for each patient (Figure 1). First, we applied a histology-based model to primary resected breast cancer tumors. Second, we then evaluated a cohort of patients undergoing neoadjuvant chemotherapy, collecting clinically relevant data including pre- and post-treatment pathology specimens, and dynamic contrast-enhanced magnetic resonance imaging. We correlated predicted outcome based on our model with actual clinical outcome, including rate of complete pathologic response (pCR) following neoadjuvant chemotherapy. We found that core biopsy samples of primary breast tumors can be used with acceptable accuracy to determine histological parameters representative of the whole tissue region. We further correlated response to neoadjuvant chemotherapy with the pretreatment tumor vasculature biomarkers and model parameters. Analysis of histology parameters, specifically radius of drug source divided by diffusion penetration distance (L/rb), a normalization penetration distance, and blood volume fraction (BVF), provides a separation of patients obtaining a pathologic complete response (pCR) and those that do not, with 80% accuracy (P = 0.0269) (Figure 2). With this predictive model, we are able to evaluate primary breast tumor vasculature biomarkers in a patient specific manner, thereby allowing a precision approach to breast cancer treatment.

Fig. 2. Histological parameters and their correlation. pCR and L/rb demonstrate a positive correlation. Dashed grey line separates patient groups with 80% accuracy.

 

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Ursa Brown-Glaberman, MD

University of New Mexico Comprehensive Cancer Center,

Albuquerque, NM 87131, USA

 

Terisse A. Brocato, PhD

Department of Chemical and Biological Engineering and Center for Biomedical Engineering,

University of New Mexico,

Albuquerque, NM 87131, USA

 

Reed Selwyn, PhD

Department of Radiology,

University of New Mexico, Albuquerque, NM 87131, USA

 

Zhihui Wang, PhD

Mathematics in Medicine Program,

Houston Methodist Research Institute, Houston, TX 77030, USA

 

Vittorio Cristini, PhD

Mathematics in Medicine Program,

Houston Methodist Research Institute, Houston, TX 77030, USA

Tel: +1 505 934 1813

vcristini@houstonmethodist.org

www.houstonmethodist.org/faculty/vittorio-cristini/

ISI Highly-Cited Researchers in Mathematics: http://highlycited.com

Google scholar: https://scholar.google.com/citations?user=uwl5tw0AAAAJ&hl=en

 

Book: “An Introduction to Physical Oncology: How Mechanistic Mathematical Modeling Can Improve Cancer Therapy Outcomes

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