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Background: This study evaluates the ability to predict the response of locally advanced breast cancers to neoadjuvant therapy (NAT) using patient-specific magnetic resonance imaging (MRI) data and a biophysical mathematical model. The 3D mathematical model consists of three parts: tumor cell proliferation, tumor spread (diffusion), and treatment. In particular, the tumor cells proliferate according to logistic growth, and the diffusion term is coupled to the mechanical properties of the surrounding fibroglandular and adipose tissues to inform individual tumor growth patterns (specific to each patient’s anatomy). The model’s treatment term accounts for tumor cell reduction according to approximate local drug delivery for each patient. Methods: Patients (N = 21) with intermediate to high grade invasive breast cancers with varying receptor status, who were eligible for NAT as a component of their clinical care, were recruited. Each patient was treated with standard-of-care consisting of one or two NAT regimens in sequence followed by surgical resection of any residual tumor. MRI data are acquired at four time points: 1) prior to initiation of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after scan 3. The MRI data is processed and evaluated using our semi-automated pipeline. Specifically, diffusion-weighted MRI data is utilized to characterize the cellularity throughout the tumor tissue, and dynamic contrast-enhanced (DCE-) MRI data is used to segment the breast tissue and analyze the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of drug concentrations. The model’s predictive ability is assessed using three different strategies. First, the model is calibrated using each patient’s first two scans to enable predictions of the total tumor cellularity, volume, and longest axis that are directly compared to the values measured from their third scan. Second, the model’s predictions for tumor response are compared to the corresponding response evaluation criteria in solid tumors (RECIST) results. Third, the model is re-calibrated using scans 3 and 4 and simulated to the time of surgery to compare the model’s predictions to each patient’s response status determined by surgical pathology. Results: Calibrating the model with MRI data for one cycle of therapy yields predictions strongly correlated with tumor response measured from each patient’s third scan, concordance correlation coefficients of 0.91, 0.90, and 0.86 for total cellularity, volume, and longest axis, respectively (p < 0.01, N = 18). The model’s predictions are significantly (p < 0.01) correlated with tumor response as designated by RECIST for the cohort. Specifically, the model predicts greater percent reduction in the longest axis for the RECIST designated responder group (i.e., complete response and partial response) compared to non-responders. At the time of surgery, the model predicts changes in total tumor cellularity from baseline that are significantly (p < 0.01) correlated with pathological response status—an area under the receiver operator characteristic curve of 0.92 and a sensitivity and specificity of 1.0 and 0.74, respectively. Discussion: These preliminary results suggest that this clinical-mathematical approach can be predictive of tumor response very early in the course of NAT on a patient-specific basis. Moreover, the study was performed in the community-care setting across a heterogenous group of patients, indicating the approach may be practical for wide-spread application. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE, R01CA240589, NCI-U24CA226110, CPRIT RR160093 Citation Format: Angela M Jarrett, David A. Hormuth, II, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Jeanne Kowalski, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Predicting breast cancer response to neoadjuvant therapies using a mathematical model individualized with patient-specific magnetic resonance imaging data: Preliminary Results [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-18.

Introduction. X-ray mammography is the standard-of-care screening protocol for breast cancer due to its low cost, widespread availability, and greater specificity. While magnetic resonance imaging (MRI) has lower specificity, it has superior tissue contrast, and dynamic contrast-enhanced (DCE) MRI has been shown to increase MRI specificity due to its ability to estimate tissue vascular properties. Because of the increased expense and scan time for MRI, there is an ongoing effort to develop abbreviated breast MRI scans for screening high-risk patients at a lower cost without compromising quantitative information. Here we investigate the effects of the limited dynamic time course afforded by an abbreviated breast MRI exam on the quantitative tissue information computed from retrospectively abbreviated DCE-MRI data. Methods. Data acquisition. We evaluate the error in perfusion model parameters computed using DCE-MRI time courses sourced from three datasets acquired with very different temporal resolutions (dt). These datasets are the ACRIN 6883 multi-site breast trial (dt = 15 s), The University of Texas at Austin (UTA) neoadjuvant therapy study (dt = 7.3 s), and The University of Chicago (UC) ultra-fast breast DCE-MRI study (dt = 3.4 s). Ten representative patients are chosen from each of these datasets for a total of 30 DCE-MRI patient datasets. All 30 full-time courses (FTCs) are retrospectively truncated into a series of abbreviated-time courses (ATCs). An ATC containing the first n post-contrast injection time points of a DCE-MRI time course is referred to as “ATC n.” For the ACRIN dataset, n is the inclusive set of integers from 7 to 18; and, similarly, for the UC data, n is the inclusive set of integers from 12 to 23. For the UTA dataset, n is the inclusive set ranging from 13 through 53, incrementing by eight. Data Analysis. The groups of FTCs and ATCs are analyzed by one of three models, determined by the specifics of the acquisition details of each dataset. The standard Kety-Tofts (SKT) model is fit to the UTA time courses, the reference-region (RR) model is fit to the ACRIN time courses, and the Patlak model is fit to the UC time courses. The volume transfer constant (Ktrans) characterizes tissue enhancement in all three models; whereas, the extravascular/extracellular volume fraction (ve) is specific to the SKT and RR models, and the plasma volume fraction (vp) is specific to the Patlak model. Due to the absence of an arterial input function (AIF) for the ACRIN dataset, the RR model was most appropriate with the pectoral muscle serving as the reference region. The UTA dataset has a population AIF and is thus able to be modeled by the SKT. Lastly, the UC dataset does not capture the tissue washout necessary for ve estimation, so the Patlak model was chosen for analyzing tissue enhancement. Results and Conclusion. The longest ATCs of 4.5, 6.4, and 1.3 min. yielded average errors of 9.1%, 7%, and 3.6% in Ktrans for the ACRIN, UTA, and UC datasets, respectively; and the shortest ATCs of 2, 1.6, and 0.6 min. yielded higher average errors of 24.2%, 22.8%, and 65% in Ktrans, respectively. This is expected from simulations as even the most aggressive ATCs did not substantially exclude the tissue enhancement, characterized by Ktrans. Errors in ve were higher overall since shorter ATCs exclude much of the washout phase. As Ktrans has been shown to discriminate between malignant and benign lesions in full length DCE-MRI scans, it is promising that tolerable errors are observed in the abbreviated MRI setting. There is potential for implementing quantitative abbreviated DCE-MRI scans in the clinic for enhanced diagnostic specificity while freeing up scan time for additional imaging sequences without interfering with standard-of-care image acquisition. Citation Format: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov. Characterizing errors in perfusion model parameters derived from retrospectively abbreviated quantitative DCE-MRI data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-26.

Introduction: The purpose of this study is to identify temporal changes in the identity and cellularity of myeloid cells infiltrating human epidermal growth factor receptor type 2 positive (HER2+) breast cancer following trastuzumab therapy. Trastuzumab is a targeted therapy used in combination with cytotoxic therapies to treat HER2+ breast cancer. Previous data shows that in HER2+ tumors, trastuzumab increases vascular maturation and decreases hypoxia, both of which can sensitize tumors to cytotoxic therapies. Preliminary immunofluorescent data reveals a significant increase in the amount of myeloid cell infiltrates in trastuzumab treated tumors during the same temporal window as vascular and hypoxia alterations. The balance of myeloid cells in the tumor microenvironment (TME) can impact neovascularization, hypoxia, and tumor progression. Thus, we hypothesize that quantifying immune modulation following trastuzumab treatment will identify mechanistic properties of trastuzumab induced vascular alterations in the TME. We present results identifying an increase in various myeloid infiltrates between control and trastuzumab treated tumors in HER2+ breast cancer. Experimental Design: BT474 HER2+ breast cancer cells were implanted subcutaneously into athymic nude mice. After reaching 250 mm3, tumors were treated with trastuzumab (10 mg/kg) or saline for one week and were excised for analysis on days 0, 4, and 7. Each tumor was cut at the largest cross-section; half was fixed in 10% neutral buffered formalin for immunohistochemistry staining of TME markers (pimonidazole, CD11c, F4/80, CD31, and alpha-SMA), and half was digested for single cell analysis using flow cytometry. Data was analyzed with FlowJo software. Distinct populations of myeloid cells have been quantified in day 4 control (n = 6) and trastuzumab treated (n = 4) tumors. A non-parametric Wilcoxon rank sum test was used to determine statistical differences. Results and Discussion: Flow analysis revealed a significant (p < 0.05) increase of macrophages in tumors treated with trastuzumab for 4 days compared to control tumors. Spatial immunofluorescent data confirms these findings showing a significant increase in macrophage populations, indicated with increased CD11c+ and F4/80+ co-staining in day 4 treated tumors compared to control (p < 0.01). Additionally, flow cytometry results show a significant (p < 0.05) increase in neutrophils in day 4 trastuzumab treated tumors compared to control tumors. Ongoing studies are evaluating spatial variations in myeloid infiltration (CD11c, F4/80), hypoxia (pimonidazole), and vascular maturation index (ratio of alpha-smooth muscle actin to total CD31 stained vessel count) through immunohistochemistry as well as comparing myeloid infiltration in trastuzumab-treated and control mice on day 7. Conclusion: Quantifying trastuzumab induced immune-modulation will provide mechanistic insight into how trastuzumab alters the TME and improves tumor sensitivity to treatment. This information will be of great importance to identifying optimal windows to administer combination therapies as well as in the development of novel immunotherapies in breast cancer. We acknowledge the support of CPRIT RR160005, NCI R01CA186193, and ACS RSG-18-006-01-CCE. Citation Format: Bloom MJ, Jarrett AM, Triplett TA, Syed AK, Yankeelov TE, Sorace AG. Investigating trastuzumab-induced myeloid cell alterations for improving combination therapy in HER2+ breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-06-17.

Introduction: Intratumoral heterogeneity is associated with variable treatment response and poorer patient prognosis. Initial changes in intratumoral cellular and vascular heterogeneity after treatment could serve as an early metric for treatment response and be used to guide and optimize therapy for improved patient outcome. Quantitative, noninvasive imaging can characterize, in 3D, tumor response to treatment by evaluating longitudinal alterations in heterogeneity. Our goal is to quantitatively image the changes in intratumoral cellular and vascular heterogeneity in response to trastuzumab in a murine model of HER2+ breast cancer. Experimental Design: Data from two independent cohorts of mice were used in this study. Mice were implanted subcutaneously with BT474 breast cancer cells (1×107) and randomly assigned to trastuzumab treated (10 mg/kg) or saline control groups. After tumors reached ~225 mm3, intraperitoneal injections of trastuzumab or saline were given on days 0 and 3. In the first cohort of mice (n=14), quantitative dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) assessed tumor response on days 0 (prior to treatment), 1 and 4. In this study, we focused on the calculated extravascular extracellular space, measured using DCE-MRI parameter, ve. In the second cohort of mice (n=10), 18F-flouromisonidazole positron emission tomography (FMISO-PET) imaging was utilized to assess tumor hypoxia on days 0 (prior to treatment), 1, 3, 4, and 7 through changes in standardized uptake value (SUV). Longitudinal changes in heterogeneity were assessed using voxel based histogram analysis of ve or SUV. Statistical analysis (R Studio) compared histogram analyses (full-width half-maximum, range, kurtosis, and standard deviation) of control and treated groups. Tumor size measurements obtained at the conclusion of the experiment were used to validate imaging findings of tumor response to treatment. Results: DCE-MRI revealed increased heterogeneity in extravascular extracellular space after trastuzumab treatment, as quantified by a significant increase in the FWHM of ve voxel distributions at day 4 post-treatment (p=0.03). Additionally, the FMISO-PET data showed a significant decrease in hypoxic regional heterogeneity in treated tumors, as quantified by percent change (normalized to baseline) in standard deviation and range of SUV tumor voxel distributions on days 3 (p=0.02), 4 (p=0.01), and 7 (p Conclusion: Trastuzumab improved oxygen delivery throughout the tumor as quantified by decreased hypoxia heterogeneity (narrowing of distributions) of the FMISO-PET SUV map. Likewise, the increased heterogeneity (widening of distributions) of the DCE-MRI ve parametric map, reflects a more heterogeneous cellularity interpreted to arise from enhanced cell death following trastuzumab treatment. Ongoing analysis of heterogeneity of corresponding histology slides will be used to validate the imaging measures of heterogeneity. DCE-MRI and FMISO-PET reveal quantitative longitudinal changes in tumor cellular and vascular heterogeneity, and may be able to predict response and guide therapy for patients with HER2+ breast cancer. Citation Format: Syed A, Quarles CC, McIntyre JO, Barnes SL, Yankeelov TE, Sorace AG. Quantitative in vivo imaging of intratumoral heterogeneity to assess tumor response to trastuzumab treatment in a preclinical model of HER2+ breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-12-12.

Introduction. Dynamic contrast-enhanced (DCE) MRI provides quantitative information on tissue properties that enhances the specificity of breast cancer diagnosis; however, mammography remains the standard screening protocol due to its lower cost. There is a push to develop more accessible abbreviated breast MRI scans for screening high-risk patients without compromising diagnostic power. Here we analyze the effects of the limited dynamic time course afforded by an abbreviated breast MRI exam on the diagnostic performance of quantitative DCE-MRI. Methods and Results. We evaluate the ability of five quantitative measures to retrospectively differentiate malignant (N=21) and benign (N=24) lesions using DCE-MRI data acquired with 15 second temporal resolution in a cohort of 45 patients from the ACRIN 6883 multi-site breast trial. The first two measures are the volume transfer constant (Ktrans) estimated by, respectively, fitting the standard Kety-Tofts (STK) perfusion model and the Patlak approximation of the STK model (Patlak model) to patient data sets that have been truncated into a series of abbreviated-time courses (ATCs). An ATC is defined as containing the first n time points of a time course and is referred to as “ATC n.” For the first measure, n is the inclusive set of integers from 7 to 14, and, for the second measure, n is the set of 4 and 7 since the Patlak approximation only holds during the initial enhancement. For each patient, the fitting procedure provides Ktrans values for each voxel within the region of interest (ROI). The values are averaged and statistically evaluated for performance in discriminating malignant from benign tumors. For Ktrans, the maximum AUC of 0.61 was achieved using ATC 14 (i.e., 3.50 minutes), while for the Patlak model, the maximum AUC of 0.55 was achieved using ATC 7 (i.e., 1.75 minutes). The third measure is a modified median signal enhancement ratio (SER) computed for a series of four ATCs per patient, where n is now the inclusive set of integers 8 through 11. The SER is defined as (S1-S0)/(S2-S0), where S0 is the pre-contrast signal, S1 is the peak enhancement, and S2 is the signal at the last time point. Abbreviating the time course effectively shifts S2 to an earlier time point. For each ATC for each patient, the SERs are computed for all voxels within the ROI after which the median is computed. We calculate the AUC under the ROC curve to evaluate the diagnostic performance of each ATC-derived median SER. ATC 10 (i.e., 2.5 minutes) yields a maximum AUC of 0.79 among all ATCs. The fourth and fifth measures are, respectively, the area under the enhancement phase and the slope of the washout phase of the patient DCE time courses. The fourth measure is computed by numerically integrating the time course of each voxel within a patient’s ROI between the first and seventh time points and averaging the values. Similarly, for the fifth measure, the slope is calculated between the seventh and last time points within the ROI and averaged. We find that the fourth measure yields an AUC under the ROC curve of 0.76, and the fifth measure yields an AUC of 0.77. Discussion and Conclusion. The highest AUC from the two pharmacokinetic measures is 0.61, which suggests ineffective diagnostic ability in this data set. The median SER computed using ATC10 yields an AUC of 0.79, showing promise as a quantitative diagnostic tool in the abbreviated scan setting. Inter-site variability and the low temporal resolution of this data set may explain the relatively lower performance of measures one, two, four, and five as compared to measure three. It is necessary to repeat this analysis using a state-of-the-art acquisition of DCE-MRI data to determine the specificity of the remaining quantitative measures to imaging specifications. Citation Format: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov. Investigating the feasibility of performing quantitative DCE-MRI in an abbreviated breast examination [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-04.

Introduction: Heterogeneity in the tumor microenvironment affects therapeutic delivery, providing a major challenge for cancer treatment. We investigate the ability of multi-parametric, voxel-based characterizations of tumor heterogeneity from quantitative magnetic resonance imaging (MRI) to spatially resolve physiological tumor subregions of response to anti-HER2 targeted therapy in a murine model of HER2+ breast cancer. Methods: BT474 cells were implanted in nude athymic mice (n=20) and tumors grown to ~225 mm3. Mice were randomly assigned to saline control or trastuzumab groups, with treatments given on days 0 and 3. Dynamic contrast enhanced (DCE) MRI and diffusion weighted (DW) MRI data were collected on days 0 (pre-treatment), 1 and 4. Tumors, excised on day 4, were processed for histology with anti-CD31 staining. Apparent diffusion coefficients (ADC, a measure of cell density) were extracted from DW-MRI data. DCE-MRI data was modeled to extract the extravascular, extracellular volume fraction, ve, and the volume transfer coefficients, Ktrans and kep, which correspond to the rate of wash-in and wash-out of contrast agent, respectively. Hierarchical clustering of tumor voxel data (ADC, ve, Ktrans, and kep) was used to identify physiological tumor subregions. The contribution of each subregion to tumor volume was quantified as percent tumor volume for each mouse and time point. Image analysis of histology data was used to generate vessel area and nuclei maps for whole-slice histology data. CD31 stained slices were subsequently divided into physiological subregions in terms of vessel and nuclei density, and the contribution of each subregion to the whole-slice area was quantified as percent tumor area. Results: Hierarchical clustering of the MRI data yielded four clusters: low vascularity - low cellularity (LV-LC), low vascularity - high cellularity (LV-HC), high vascularity - low cellularity (HV-LC), high vascularity - high cellularity (HV-HC). At day 0, no significant differences in cluster percent tumor volume were observed between control and treated tumors (p>0.05). At day 4, a significant decrease in LV-HC percent tumor volume was observed in treated tumors compared to control (mean 13.7% vs. 35.0%, p=0.04). Histology subregion analysis corroborated in vivo imaging findings, with treated tumors having significantly lower LV-HC percent tumor area compared to control (mean 26.8% vs 53.6%, p Conclusion: High-dimensional analysis of quantitative MRI parameter maps can be utilized to identify physiological subregions of tumor response, and can be biologically validated using histology data. The results suggest trastuzumab therapy decreases the hypoxic (LV-HC) tumor volume. Quantifying tumor microenvironment alterations in response to therapy can potentially be used to predict response for patients with HER2+ breast cancer. Citation Format: Anum Syed, Jennifer Whisenant, Anna Sorace, Thomas Yankeelov. Quantifying trastuzumab-induced alterations of intratumoral heterogeneity using MRI-derived tumor subregions in the preclinical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1946.

Purpose: Optimization of drug synergy has potential to improve efficacy of combination treatments. Breast cancer that overexpresses the human epidermal growth factor receptor (HER2) has a higher chance of metastasis and recurrence compared to HER2- patients. Standard-of-care treatment typically includes chemotherapy (i.e., paclitaxel, doxorubicin) followed by or in combination with targeted antibody treatments (i.e., trastuzumab, pertuzumab). The purpose of this study is to investigate, the timing, order, and dosage of standard-of-care combination therapies and how they affect the overall efficacy of treatment in HER2+ breast cancer in vitro and in vivo. Experimental Procedure: BT474, HER2+ breast cancer cells were longitudinally evaluated with time resolved microscopy to measure changes in cell confluence (Incucyte). Cells were treated with combinations of trastuzumab (10-50 ug/ml) and paclitaxel (10-150 nmol/L) with alternating sequencing and timing (24, 48 hrs) of the therapies (N=8 replicates per combination). Cellular confluence was measured over 4 days and normalized percent changes were calculated. BT474 tumor-bearing mice (N=42) were treated singularly or in combination with alternating sequencing of trastuzumab (10 mg/kg) and doxorubicin (1.5 mg/kg). Tumors were evaluated for changes in tumor size and survival. An unpaired t-test was used for statistical comparisons for in vitro and in vivo studies. Assessment of cellular and tumor microenvironment influences on drug synergy are ongoing. Results: Experimental outcomes indicate that alternating order and dose play a crucial role in the anticancer efficacy and synergistic drug effects. Trastuzumab (25 ug/ml) dosing prior to paclitaxel (50 nmol) significantly decreased cell confluence compared to the same total therapeutic dose where paclitaxel was introduced first in vitro (p < 0.001). Similarly, trastuzumab dosing in vivo prior to a one dose of chemotherapy achieves the same tumor response as a two doses of chemotherapy administered simultaneously with trastuzumab (p < 0.001). Discussion and Conclusions: Sequencing of targeted and chemotherapies altered treatment response both in vivo and in vitro, resulting in differences in overall cancer cell death. In vitro longitudinal imaging can provide numerous combinations of treatments can be tested to ultimately find optimal treatments to test in vivo. Recent evidence also shows that targeted anti-HER2 therapy can alter vascularity, oxygenation, and immune cell infiltration in HER2+ breast cancer tumors; therefore, further investigations in the temporal effects of the tumor microenvironment are warranted. Improved temporal windows of anticancer effects for standard-of-care combination therapies could improve breast cancer treatment regimens and potentially minimize toxic side effects. Funding was provided by CPRIT RR160005 and ACS-RSG-18-006-01-CCE. Citation Format: Alay Shah, Angela Jarrett, Anum Syed, Thomas Yankeelov, Anna Sorace. Trastuzumab sensitizes HER2+ breast cancer to increase efficacy of chemotherapy in vitro and in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 296.

Introduction: The purpose of this study is to quantify longitudinal changes in myeloid cell infiltration after trastuzumab treatment to reveal mechanistic properties of improved tumor vascularization in human epidermal growth factor receptor type 2 positive (HER2+) breast cancer. We have previously presented in vivo data showing that trastuzumab increases vascular maturation leading to better delivery of therapeutics. The balance of myeloid cells in the tumor microenvironment (TME) can impact neovascularization. Specifically, M2 macrophages (MACs) promote angiogenesis causing irregular vascularization and M1 MACs exhibit anti-angiogenic properties counteracting the M2 cells and improving tumor perfusion. We present results revealing macrophage polarization towards an M1 phenotype after trastuzumab therapy in HER2+ tumors. Experimental Design: 107 BT474 breast cancer cells were implanted subcutaneously into athymic nude mice. After tumors reached 250 mm3, mice were treated with trastuzumab (10 mg/kg) or saline over one week and were excised for analysis on days 0, 4 and 7. Half of each tumor was fixed in 10% neutral buffered formalin for immunohistochemical staining (IHC) of the TME and half was digested for analysis of myeloid cell infiltration with flow cytometry. The flow cytometry panel used consists of ten markers, two specifically for the identification of M1 and M2 MACs; CD38, and CD206, respectively. A non-parametric Wilcoxon rank sum test was used to determine statistical differences between control and treated groups. Results and Discussion: Initial studies revealed an increase (P = 0.03) of MACs in day 4 and day 7 treated tumors. Of these MACs, there was an increase (P = 0.03) in co-expression of M2 and M1 phenotype markers CD206 and CD38 in day 4 treated tumors indicating a phenotypic switch occurring. After a third dose of trastuzumab, there was an increase (P = 0.03) in M1 MACs (CD38+/CD206-) in day 7 treated tumors. In day 4 and 7 treated tumors, there was a decrease (P = 0.02) in non-differentiated MACs (CD38-/CD206-) supporting evidence that a phenotypic switch is occurring. Ongoing studies are evaluating spatial variations in MAC infiltration (CD206, F4/80), hypoxia (pimonidazole), and vascular maturation index (ratio of alpha-smooth muscle actin to total vessel counts as stained with CD31) through IHC. Cytokine detection assays are being done to evaluate changes in cytokines contributing to macrophage polarization and angiogenesis. Conclusion: These results offer strong evidence supporting the hypothesis that MACs in the TME are switching to an M1 phenotype after trastuzumab treatment and contributing to trastuzumab induced vascular alterations. Analyses of IHC will validate vascular changes occurring simultaneously with myeloid infiltratration. Citation Format: Meghan J. Bloom, Angela M. Jarrett, Todd A. Triplett, Anum K. Syed, Thomas E. Yankeelov, Anna G. Sorace. Trastuzumab induced myeloid cell alterations identifies mechanistic properties of vascular normalization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 187.

Introduction: Trastuzumab, a targeted therapy for human epidermal growth factor receptor 2 (HER2) positive breast cancer, induces cell cycle arrest and inhibits HER2 expression. Trastuzumab has been shown to improve vascular delivery of subsequent cytotoxic therapies, but the mechanism by which it regulates tumor-associated angiogenesis is not well characterized. Therefore, we developed an integrated, mathematical-experimental approach to systematically investigate the interactions of tumor-growth characteristics—vasculature, immune response, hypoxia, and necrosis—and to evaluate their effects on tumor response to treatment in a murine model of HER2+ breast cancer. Experimental: Mice (N=102) were injected subcutaneously with HER2-overexpressing BT474 breast cancer cells and treated with trastuzumab (10 mg/kg) or saline. Tumor volumes for two separate cohorts of treated and control mice were recorded longitudinally, and either mice were quantitatively imaged or tumors extracted for histology over seven days. Necrosis, vasculature, and hypoxia were characterized by: immunohistochemistry for percent necrosis, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the percentage of well-vascularized tissue, and 18F-fluoromisonidazole positron emission tomography (FMISO-PET) for percent hypoxia. Preliminary immune (myeloid) cell infiltration was quantified using immunofluorescence of F4/80 and CD11c. Mathematical Model: A system of five coupled, ordinary differential equations describing the temporal variation in tumor growth, vasculature, hypoxia, necrosis, and immune response was calibrated using data for tumor volume and percentages of well-vascularized tissue, hypoxia, and necrosis. After validation using a second tumor volume data set, uncertainty analysis was used to verify plausible overlap between the model's simulations and the experimental data when considering calibration error, and sensitivity analysis identified critical parameters for experimental estimation and potential model reductions. Results and Discussion: The calibrated model was used to predict immune response behavior over time and yielded different results between the two groups—stagnant versus increasing immune component values for control versus treated mice, respectively. Preliminary immunofluorescence data support the immunological predictions of the model; in particular, F4/80 and CD11c co-staining in the total tissue is greater in trastuzumab treated than control tumors (p < 0.03). Furthermore, differences in the calibrated parameter sets indicate several experimentally testable hypotheses, including increased cross-talk between immune cells and vasculature as a mechanism for vascular stabilization due to trastuzumab therapy. We acknowledge the support of CPRIT RR160005 and NCI R01CA186193. Citation Format: Angela M. Jarrett, Meghan Bloom, Wesley Godfrey, Anum Syed, David A. Ekrut, Lauren I. Ehrlich, Thomas E. Yankeelov, Anna G. Sorace. A mathematical-experimental approach for predicting host responses in a preclinical model for trastuzumab-treated HER2+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 924.

Introduction: We investigate the ability of multi-parametric, voxel-based characterizations of tumor heterogeneity from magnetic resonance imaging (MRI) to predict the response of breast cancer patients to neoadjuvant therapy (NAT). In particular, we use high-dimensional analysis of the longitudinal changes in the vascular and cellular characteristics provided by quantitative dynamic contrast enhanced MRI (DCE-MRI) to predict response. Methods: DCE-MRI data was acquired from 34 patients with stage II/III breast cancer before initiating NAT (t1) and after one cycle of NAT (t2). Pathological complete response (pCR) and non-pCR was defined at the time of surgery. Non-pCR patients were further subdivided into partial response (>30% decrease in tumor volume), progressive disease (PD, >20% increase in tumor volume), and stable disease according to the Response Evaluation in Solid Tumors (i.e., RECIST) criteria. For each tumor voxel, DCE-MRI data was modeled to extract the extravascular, extracellular volume fraction, ve, the plasma volume fraction, vp, and the volume transfer coefficients, Ktrans and kep, which correspond to the rate of wash-in and wash-out of contrast agent, respectively. Multi-parametric voxel-based maps of physiological parameters Ktrans, kep, ve, and vp were used to identify different tumor subpopulations. Dimension reduction was completed using t-distributed Stochastic Neighbor Embedding (t-SNE) and subpopulations were identified using DBSCAN clustering. Using the t-SNE generated low-dimensional maps, a subset subpopulations were analyzed based on cluster differences between groups at t2. Based on MRI parameter values, the selected voxel subpopulations fell into one of two categories: high vascularity-high cellularity (HV-HC: high vp , Ktrans , kep values, low ve values) and low vascularity-low cellularity (LV-LC: low vp , Ktrans , kep values, high ve values). The contribution of each cluster to overall tumor volume was analyzed for each patient and quantified as percent tumor volume. Results: No differences were observed between patient groups at t1. At t2, patients with pCR revealed increased percent tumor volumes containing LV-LC subpopulations, compared to PD patients (p = 0.03). Furthermore, PD patients demonstrated significant increase in percent tumor volumes with HV-HC subpopulations, compared to pCR patients (p=0.01). Compared to pCR patients, combined non-pCR patients showed decreased percent tumor volumes of LV-LC subpopulations, trending toward significance (p=0.06). Conclusion: The results indicate that analysis of high-dimensional parameter maps derived from quantitative MRI can be utilized to characterize intratumoral heterogeneity and identify subpopulations of tumor response within patients. These characterizations can potentially be used to define and model tumor habitats and ultimately predict patient treatment response. Citation Format: Anum Syed, Anna G. Sorace, Stephanie L. Barnes, Lori Arlinghaus, Xia Li, Thomas E. Yankeelov. Assessing heterogeneity in DCE-MRI data of breast cancer to predict treatment response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3941. doi:10.1158/1538-7445.AM2017-3941