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Immune checkpoint inhibitors (ICI) are promising in adjuvant settings for solid tumors and hematologic malignancies. They are currently used in the treatment as mAbs in high concentrations, raising concerns of toxicity and adverse side effects. Among various checkpoint molecules, targeting the programmed cell death protein-1 (PD-1)–programmed death-ligand 1 (PD-L1) axis has garnered more clinical utility than others have. To develop a physiologically relevant and systemically stable level of ICIs from a one-time application by genetic antibody engineering, we endeavored using a nonpathogenic, replication-deficient recombinant adeno-associated vector (rAAV) expressing single-chain variable fragments (scFv) of PD-L1 antibody and tested in syngeneic mouse therapy models of MC38 colorectal and EMT6 breast tumors. Results of this study indicated a significant protection against PD-L1–mediated inhibition of CD8+ T-cell function, against the growth of primary and secondary tumors, and durable antitumor CTLs activity by adoptive CD8+ T-cell transfer. Stable maintenance of PD-L1 scFv in vivo resulted in an increase in PD-1− CD8+ T cells and a concomitant decrease in regulatory T cells, M2 macrophages, and myeloid-derived suppressor cells in the tumor microenvironment. Overall, these data demonstrate the potential of rAAV-PD-L1-scFv as an alternative to mAb targeting of PD-L1 for tumor therapy.

Metastases account for the majority of mortalities related to breast cancer. The onset and sustained presence of hypoxia strongly correlates with increased incidence of metastasis and unfavorable prognosis in patients with breast cancer. The Hedgehog (Hh) signaling pathway is dysregulated in breast cancer, and its abnormal activity enables tumor progression and metastasis. In addition to programming tumor cell behavior, Hh activity enables tumor cells to craft a metastasis-conducive microenvironment. Hypoxia is a prominent feature of growing tumors that impacts multiple signaling circuits that converge upon malignant progression. We investigated the role of Hh activity in crafting a hypoxic environment of breast cancer. We used radioactive tracer [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET) to image tumor hypoxia. We show that tumors competent for Hh activity are able to establish a hypoxic milieu; pharmacologic inhibition of Hh signaling in a syngeneic mammary tumor model mitigates tumor hypoxia. Furthermore, in hypoxia, Hh activity is robustly activated in tumor cells and institutes increased HIF signaling in a VHL-dependent manner. The findings establish a novel perspective on Hh activity in crafting a hypoxic tumor landscape and molecularly navigating the tumor cells to adapt to hypoxic conditions.Implications: Importantly, we present a translational strategy of utilizing longitudinal hypoxia imaging to measure the efficacy of vismodegib in a preclinical model of triple-negative breast cancer.

Supplementary Figure from Systemic Checkpoint Blockade by PD-L1 Single-Chain Antibody Confers Potent Antitumor Immunity and Long-term Survival

Supplementary Figure from Quantitative Longitudinal Imaging Reveals that Inhibiting Hedgehog Activity Alleviates the Hypoxic Tumor Landscape

Supplementary Data from Quantitative Longitudinal Imaging Reveals that Inhibiting Hedgehog Activity Alleviates the Hypoxic Tumor Landscape

Immune checkpoint inhibitors (ICI) are promising in adjuvant settings for solid tumors and hematologic malignancies. They are currently used in the treatment as mAbs in high concentrations, raising concerns of toxicity and adverse side effects. Among various checkpoint molecules, targeting the programmed cell death protein-1 (PD-1)–programmed death-ligand 1 (PD-L1) axis has garnered more clinical utility than others have. To develop a physiologically relevant and systemically stable level of ICIs from a one-time application by genetic antibody engineering, we endeavored using a nonpathogenic, replication-deficient recombinant adeno-associated vector (rAAV) expressing single-chain variable fragments (scFv) of PD-L1 antibody and tested in syngeneic mouse therapy models of MC38 colorectal and EMT6 breast tumors. Results of this study indicated a significant protection against PD-L1–mediated inhibition of CD8+ T-cell function, against the growth of primary and secondary tumors, and durable antitumor CTLs activity by adoptive CD8+ T-cell transfer. Stable maintenance of PD-L1 scFv in vivo resulted in an increase in PD-1− CD8+ T cells and a concomitant decrease in regulatory T cells, M2 macrophages, and myeloid-derived suppressor cells in the tumor microenvironment. Overall, these data demonstrate the potential of rAAV-PD-L1-scFv as an alternative to mAb targeting of PD-L1 for tumor therapy.

Purpose:Hypoxia is a common characteristic of many tumor microenvironments, and it has been shown to promote suppression of antitumor immunity. Despite strong biological rationale, longitudinal correlation of hypoxia and response to immunotherapy has not been investigated.Experimental Design:In this study, we probed the tumor and its surrounding microenvironment with 18F-FMISO PET imaging to noninvasively quantify tumor hypoxia in vivo prior to and during PD-1 and CTLA-4 checkpoint blockade in preclinical models of breast and colon cancer.Results:Longitudinal imaging identified hypoxia as an early predictive biomarker of therapeutic response (prior to anatomic changes in tumor volume) with a decreasing standard uptake value (SUV) ratio in tumors that effectively respond to therapy. PET signal correlated with ex vivo markers of tumor immune response including cytokines (IFNγ, GZMB, and TNF), damage-associated molecular pattern receptors (TLR2/4), and immune cell populations (macrophages, dendritic cells, and cytotoxic T cells). Responding tumors were marked by increased inflammation that were spatially distinct from hypoxic regions, providing a mechanistic understanding of the immune signaling pathways activated. To exploit image-guided combination therapy, hypoxia signal from PET imaging was used to guide the addition of a hypoxia targeted treatment to nonresponsive tumors, which ultimately provided therapeutic synergy and rescued response as determined by longitudinal changes in tumor volume.Conclusions:The results generated from this work provide an immediately translatable paradigm for measuring and targeting hypoxia to increase response to immune checkpoint therapy and using hypoxia imaging to guide combinatory therapies.

Supplementary Data from 18F-FMISO PET Imaging Identifies Hypoxia and Immunosuppressive Tumor Microenvironments and Guides Targeted Evofosfamide Therapy in Tumors Refractory to PD-1 and CTLA-4 Inhibition

Neurobehavioral disorders and brain abnormalities have been extensively reported in both Crohn’s disease and ulcerative colitis patients. However, the mechanism causing neuropathological disorders in inflammatory bowel disease patients remains unknown. Studies have linked the Th17 subset of CD4+ T cells to brain diseases associated with neuroinflammation and cognitive impairment, including multiple sclerosis, ischemic brain injury, and Alzheimer’s disease. To better understand how CD4+ T lymphocytes contribute to brain pathology in chronic intestinal inflammation, we investigated the development of brain inflammation in the T cell transfer model of chronic colitis. Our findings demonstrate that CD4+ T cells infiltrate the brain of colitic Rag1−/− mice in proportional levels to colitis severity. Colitic mice developed hypothalamic astrogliosis that correlated with neurobehavioral disorders. Moreover, the brain-infiltrating CD4+ T cells expressed Th17 cell transcription factor retinoic acid–related orphan receptor γt (RORγt) and displayed a pathogenic Th17 cellular phenotype similar to colonic Th17 cells. Adoptive transfer of RORγt-deficient naive CD4+ T cells failed to cause brain inflammation and neurobehavioral disorders in Rag1−/− recipients, with significantly less brain infiltration of CD4+ T cells. The finding is mirrored in chronic dextran sulfate sodium–induced colitis in Rorcfl/fl Cd4-Cre mice that showed lower frequency of brain-infiltrating CD4+ T cells and astrogliosis despite onset of significantly more severe colitis compared with wild-type mice. These findings suggest that pathogenic RORγt+CD4+ T cells that aggravate colitis migrate preferentially into the brain, contributing to brain inflammation and neurobehavioral disorders, thereby linking colitis severity to neuroinflammation.

Navigating parental leave can be challenging in all fields of medicine, but it can be especially challenging for leaders balancing clinical, research, and administrative duties. As women take on more leadership roles, we have the opportunity to better define the current challenges and identify potential strategies for navigating successful parental leave while balancing the demands of leadership. This manuscript provides a commentary on the challenges and strategies for navigating parental leave in leadership positions in radiology, an important topic for shaping how parental leave is both viewed and valued in the future. Specifically, we highlight challenges and strategies for administrative responsibilities, reporting personnel, emails, microaggressions, research, empowerment, and prioritization.

Metastases account for the majority of mortalities related to breast cancer. The onset and sustained presence of hypoxia strongly correlates with increased incidence of metastasis and unfavorable prognosis in breast cancer patients. The Hedgehog (Hh) signaling pathway is dysregulated in breast cancer, and its abnormal activity enables tumor progression and metastasis. In addition to programming tumor cell behavior, Hh activity enables tumor cells to craft a metastasis-conducive microenvironment. Hypoxia is a prominent feature of growing tumors that impacts multiple signaling circuits that converge upon malignant progression. We investigated the role of Hh activity in crafting a hypoxic environment of breast cancer. We used radioactive tracer [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET) to image tumor hypoxia. We show that tumors competent for Hh activity are able to establish a hypoxic milieu; pharmacological inhibition of Hh signaling in a syngeneic mammary tumor model mitigates tumor hypoxia. Furthermore, in hypoxia, Hh activity is robustly activated in tumor cells and institutes increased HIF signaling in a VHL-dependent manner. The findings establish a novel perspective on Hh activity in crafting a hypoxic tumor landscape and molecularly navigating the tumor cells to adapt to hypoxic conditions. Importantly, we present a translational strategy of utilizing longitudinal hypoxia imaging to measure the efficacy of Vismodegib in a preclinical model of triple-negative breast cancer. Implications: Using clinically relevant FMISO-PET imaging can be effectively utilized to monitor response to Vismodegib therapy.

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2–3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction–diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor’s size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient’s tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

Determination of treatment response to immunotherapy in glioblastoma multiforme (GBM) is a process which can take months. Detection of CD8+ T cell recruitment to the tumor with a noninvasive imaging modality such as positron emission tomography (PET) may allow for tumor characterization and early evaluation of therapeutic response to immunotherapy. In this study, we utilized 89Zr-labeled anti-CD8 cys-diabody-PET to provide proof-of-concept to detect CD8+ T cell immune response to oncolytic herpes simplex virus (oHSV) M002 immunotherapy in a syngeneic GBM model. Immunocompetent mice (n = 16) were implanted intracranially with GSC005 GBM tumors, and treated with intratumoral injection of oHSV M002 or saline control. An additional non-tumor bearing cohort (n = 4) receiving oHSV M002 treatment was also evaluated. Mice were injected with 89Zr-labeled anti-CD8 cys-diabody seven days post oHSV administration and imaged with a preclinical PET scanner. Standardized uptake value (SUV) was quantified. Ex vivo tissue analyses included autoradiography and immunohistochemistry. PET imaging showed significantly higher SUV in tumors which had been treated with M002 compared to those without M002 treatment (p = 0.0207) and the non-tumor bearing M002 treated group (p = 0.0021). Accumulation in target areas, especially the spleen, was significantly reduced by blocking with the non-labeled diabody (p + cell trafficking patterns after oHSV treatment. This PET imaging strategy could aid in distinguishing responders from non-responders during immunotherapy of GBM.

Background: Although a portion of triple negative breast cancer (TNBC) is sensitive to chemotherapeutic treatment with agents such as paclitaxel (PTX), patients have a high risk of recurrence and short overall and progression-free survival. Clinical trials for TNBC using immune checkpoint inhibitors such as anti-programmed cell death 1 (PD1) have met some success; however, only a small percentage of patients have a positive response to immunotherapy and there is risk for severe side effects. Therefore, there is a need for improvements in monitoring and predicting patient-specific response. The goal of this study is to use non-invasive visualization of the tumor microenvironment to collect longitudinal information that can be used to study tumor response to immunotherapy. By applying positron emission tomography (PET) imaging techniques to investigate changes in the tumor microenvironment (hypoxia and immune cell activation), we seek to predict early response to immunotherapy and better identify which treatments will be effective for individual tumors in TNBC. Methods: A TNBC mouse mammary carcinoma cell line, 4T1, was transduced with CMV-luciferase and 2x105 cells were injected into the third mammary fat pad of 5-6 week old female Balb/c mice (N=28). Mice in cohort 1 received: PTX (10 mg/kg), anti-PD1 (200 µg), both, or vehicle control (saline) (n=4/group). Mice in cohort 2 received: anti-PD1 (n=5), combination PTX/anti-PD1 (n=4), or vehicle control (saline; n=3). Treatments were administered intraperitoneally on days 0, 2, and 5 for cohort 1 (n=16) who underwent granzyme B-PET imaging ([68Ga]-NOTA-GZP-PET) and on days 0, 2, 5, and 8 for cohort 2 (n=12) who underwent hypoxia imaging with [18F]-fluoromisonidazole (FMISO)-PET imaging. Bioluminescence (BLI) imaging and caliper measurements were performed to track tumor size changes at multiple timepoints and tumors were collected for histological validation on day 20. Mean standard uptake value (SUVmean) was calculated as percent of day 0, and statistical analyses were performed with unpaired t-tests and Wilcoxon-rank sum tests. Results: Voxel analysis of GZP-PET images revealed an 42.9% increase in T cell activation of TNBC tumors treated with single-agent PTX compared to anti-PD1 alone on day 3 (p=0.08). FMISO-PET revealed that tumors treated with anti-PD1 alone had lower hypoxic fraction compared to control group tumors (p=0.10) and tumors treated with combination PTX/anti-PD1 (p=0.17) on day 3. BLI data showed that treatment with PTX and anti-PD1 significantly decreased viability signal between days 3 and 6 for cohort 1 (p=0.04). Non-responders to treatment had a significantly higher tumor volume compared to responders starting on day 6 (p Citation Format: Tiara S. Napier, Chanelle L. Hunter, Patrick N. Song, Ben M. Larimer, Anna G. Sorace. Molecular imaging of hypoxia and granzyme B alterations during combination treatment with immunotherapy in triple negative breast cancer [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 PS3-07.

Introduction: Tumor hypoxia contributes to intra-tumoral heterogeneity and decreases efficacy of cytotoxic therapy. Fractionated radiation therapy has emerged as an alternative to single dose radiation, and is a component of standard-of-care therapy for patients with unresectable human epidermal growth factor receptor 2 (HER2+) breast cancer. Because radiation therapy is dependent on tissue oxygenation, therapies that increase oxygenation could radio-sensitize tumors. The goal of this study is to investigate if molecular imaging with [18F]-fluoromisonidazole (FMISO)-PET can quantify anti-HER2 therapy-induced changes in tumor oxygenation and utilize imaging metrics to enhance the effectiveness of fractionated radiation. Improving treatment synergy in HER2+ breast cancer has potential to increase therapeutic effectiveness without increasing cytotoxic therapy. Methods: For in vitro studies, HER2+ breast cancer cells (BT474, SKBR3, MDA-MB-361 and MDA-MB-453) were treated with various sequencing of combination trastuzumab (1 µg/mL) and fractionated radiation (6/3 Gy) and assessed for cell death. HER2+ cancer cells (BT474 and MDA-MB-361) were also treated with combination trastuzumab and fractionated radiation and analyzed for DNA double strand breaks (DSB) through flow cytometry. For in vivo studies, HER2+ cell line (BT474 and MDA-MB-361) and HER2+ patient derived xenograft (BCM 3472) tumors were engrafted into mice and treated with trastuzumab (4 mg/kg) on days 0 and 3 and fractionated radiation (6/3 Gy) on days 1, 2 and 3 (or single agent control). [18F]-FMISO-PET imaging was conducted on day 0, 3 and 7. At the imaging endpoint, tumors were either extracted for biological validation or continued to measure tumor size changes for longitudinal assessment of response. Bliss test of independence and a non-parametric T-test was used to assess for treatment synergy and significance, respectively. Results: In vitro cell death assay revealed single agent trastuzumab or fractionated radiation treated groups exhibited 15.2% ± 6.5% or 53.6% ± 9% cell death respectively, while trastuzumab prior to fractionated radiation groups exhibited 72.5% ± 2.5% cell death (p = 0.01) on day 7 in MDA-MB-361 cells. Flow cytometry analysis showed MDA-MB-361 cells treated with trastuzumab prior to fractionated radiation exhibited 62.4% ± 8.7% DSB, which significantly increased from single agent trastuzumab (0.74% ± 0.39%) or fractionated radiation groups (37.3% ± 7.3%) (p = 0.01). In vivo, MDA-MB-361 tumors treated with trastuzumab and fractionated radiation had a [18F]-FMISO SUVmean of 0.20 ± 0.09, while tumors treated with fractionated radiation had a [18F]-FMISO SUVmean of 0.31 ± 0.06 on day 7 (p = 0.05). MDA-MB-361 tumors treated with trastuzumab and fractionated radiation experienced a 26.1% ± 16.8% decrease in tumor volume, while tumors treated with single agent fractionated radiation experienced a 10.4% ± 2.8% decrease in tumor volume from day 0 to day 7 (p = 0.11). 30 days after start of therapy, MDA-MB-361 tumors treated with single agent fractionated radiation had a tumor volume of 471.8 ± 120 mm3, whereas tumors treated with combination trastuzumab and fractionated radiation had a tumor volume of 116 ± 38 mm3 (p < 0.01). Bliss test of independence confirmed in vivo treatment synergy of trastuzumab and fractionated radiation starting 14 days after start of therapy. Conclusion: HER2+ breast cancer treated with trastuzumab prior to fractionated radiation synergistically increases efficacy of radiotherapy in vitro and in vivo. [18F]-FMISO-PET imaging has potential to identify in vivo response to combination therapies and better understand changes in the tumor microenvironment to guide combination therapy. Acknowledgements: We thank the American Cancer Society for RSG-18-006-01-CCE and NIH NCI R01 CA240589. Citation Format: Patrick N. Song, Yun Lu, Tiara Napier, Sharon Samuel, Katherine Heinzman, Suzanne E. Lapi, Anna G. Sorace. Identifying efficacy of targeted HER2 antibodies in sensitization of HER2 positive breast cancer to fractionated radiation [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 PS10-21.

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.

The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.

The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.

Maximal safe resection of malignant tissue is associated with improved progression-free survival and better response to radiation and chemotherapy for patients with glioblastoma (GBM). 5-Aminolevulinic acid (5-ALA) is the current FDA-approved standard for intraoperative brain tumor visualization. Unfortunately, autofluorescence in diffuse areas and high fluorescence in dense tissues significantly limit discrimination at tumor margins. This study is the first to compare 5-ALA to an investigational new drug, panitumumab-IRDye800CW, in the same animal model. A patient-derived GBM xenograft model was established in 16 nude mice, which later received injections of 5-ALA, panitumumab-IRDye800CW, IRDye800CW, 5-ALA and IRDye800CW, or 5-ALA and panitumumab-IRDye800CW. Brains were prepared for multi-instrument fluorescence imaging, IHC, and quantitative analysis of tumor-to-background ratio (TBR) and tumor margin accuracy. Statistical analysis was compared with Wilcoxon rank-sum or paired t test. Panitumumab-IRDye800CW had a 30% higher comprehensive TBR compared with 5-ALA (P = 0.0079). SDs for core and margin regions of interest in 5-ALA–treated tissues were significantly higher than those found in panitumumab-IRDye800CW–treated tissues (P = 0.0240 and P = 0.0284, respectively). Panitumumab-IRDye800CW specificities for tumor core and margin were more than 10% higher than those of 5-ALA. Higher AUC for panitumumab-IRDye800CW indicated strong capability to discriminate between normal and malignant brain tissue when compared with 5-ALA. This work demonstrates that panitumumab-IRDye800CW shows potential as a targeting agent for fluorescence intraoperative detection of GBM. Improved margin definition and surgical resection using panitumumab-IRDye800 has the potential to improve surgical outcomes and survival in patients with GBM compared with 5-ALA.

Inflammatory breast cancer (IBC), a rare form of breast cancer associated with increased angiogenesis and metastasis, is largely driven by tumor-stromal interactions with the vasculature and the extracellular matrix (ECM). However, there is currently a lack of understanding of the role these interactions play in initiation and progression of the disease. In this study, we developed the first three-dimensional, in vitro, vascularized, microfluidic IBC platform to quantify the spatial and temporal dynamics of tumor-vasculature and tumor-ECM interactions specific to IBC. Platforms consisting of collagen type 1 ECM with an endothelialized blood vessel were cultured with IBC cells, MDA-IBC3 (HER2+) or SUM149 (triple negative), and for comparison to non-IBC cells, MDA-MB-231 (triple negative). Acellular collagen platforms with endothelialized blood vessels served as controls. SUM149 and MDA-MB-231 platforms exhibited a significantly (p < .05) higher vessel permeability and decreased endothelial coverage of the vessel lumen compared to the control. Both IBC platforms, MDA-IBC3 and SUM149, expressed higher levels of vascular endothelial growth factor (p < .05) and increased collagen ECM porosity compared to non-IBCMDA-MB-231 (p < .05) and control (p < .01) platforms. Additionally, unique to the MDA-IBC3 platform, we observed progressive sprouting of the endothelium over time resulting in viable vessels with lumen. The newly sprouted vessels encircled clusters of MDA-IBC3 cells replicating a key feature of in vivo IBC. The IBC in vitro vascularized platforms introduced in this study model well-described in vivo and clinical IBC phenotypes and provide an adaptable, high throughput tool for systematically and quantitatively investigating tumor-stromal mechanisms and dynamics of tumor progression.

Background Therapy targeted to the human epidermal growth factor receptor type 2 (HER2) is used in combination with cytotoxic therapy in treatment of HER2+ breast cancer. Trastuzumab, a monoclonal antibody that targets HER2, has been shown pre-clinically to induce vascular changes that can increase delivery of chemotherapy. To quantify the role of immune modulation in treatment-induced vascular changes, this study identifies temporal changes in myeloid cell infiltration with corresponding vascular alterations in a preclinical model of HER2+ breast cancer following trastuzumab treatment. Methods HER2+ tumor-bearing mice (N = 46) were treated with trastuzumab or saline. After extraction, half of each tumor was analyzed by immunophenotyping using flow cytometry. The other half was quantified by immunohistochemistry to characterize macrophage infiltration (F4/80), vascularity (CD31 and α-SMA), proliferation (Ki67) and cellularity (H&E). Additional mice (N = 10) were used to quantify differences in tumor cytokines between control and treated groups. Results Immunophenotyping showed an increase in macrophage infiltration 24 h after trastuzumab treatment (P ≤ 0.05). With continued trastuzumab treatment, the M1 macrophage population increased (P = 0.02). Increases in vessel maturation index (i.e., the ratio of α-SMA to CD31) positively correlated with increases in tumor infiltrating M1 macrophages (R = 0.33, P = 0.04). Decreases in VEGF-A and increases in inflammatory cytokines (TNF-α, IL-1β, CCL21, CCL7, and CXCL10) were observed with continued trastuzumab treatment (P ≤ 0.05). Conclusions Preliminary results from this study in a murine model of HER2+ breast cancer show correlations between immune modulation and vascular changes, and reveals the potential for anti-HER2 therapy to reprogram immunosuppressive components of the tumor microenvironment. The quantification of immune modulation in HER2+ breast cancer, as well as the mechanistic insight of vascular alterations after anti-HER2 treatment, represent novel contributions and warrant further assessment for potential clinical translation.

Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitative magnetic resonance imaging (MRI) data acquired early in the course of NAT into a mechanism-based, biophysical model that predicts the eventual treatment response of breast tumors. Being able to predict which patient will respond effectively to NAT would have a fundamental and lasting impact on healthcare. However, the ultimate goal is to optimize therapy given the unique characteristics of each patient. Here we show that the detailed combination of advanced image analysis and rigorous mathematical modeling can accurately predict response for the individual patient. Further, we use the model to demonstrate the potential selection of personalized therapeutic regimens. This is accomplished by initializing the mathematical model with patient-specific characteristics and then varying, in silico, a range of treatment plans to achieve the greatest tumor control. Methods: Quantitative MRI was acquired from breast cancer patients (N = 11) at three time points during the course of NAT: 1) prior to NAT, 2) after 1 cycle of their initial chemotherapy, and 3) after the completion of the initial chemotherapy regimen. With these data, we implemented our recently established mechanically coupled, reaction-diffusion model at the tissue scale for predicting breast tumor response to therapy. The 3D model is initialized with patient-specific, diffusion-weighted MRI data characterizing tumor cellularity. Additionally, the model includes a tumor cell reduction term for local drug delivery as estimated from pharmacokinetic analysis of dynamic contrast-enhanced MRI data and population-derived plasma curves of therapeutic concentrations. The model’s predictive ability was assessed using three different measures. Using the first two scans, the model is calibrated and simulated forward to the third scan time to compare the predicted total tumor cellularity, volume, and longest axis to the actual values measured from the patient’s third scan. We then simulate alternate regimens using the same total dose each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Results: After calibrating the model using the first two imaging time points, the model’s predictions are significantly correlated to the measured tumor burden at scan three with Pearson Correlation Coefficients of 0.93, 0.89, 0.96 (p < 0.01) for total cellularity, total volume, and longest axis, respectively. The model predicts that for the alternative dosing regimens assessed, individual patients could have achieved an additional 0-43% reduction in total cellularity compared to the therapeutic regimens patients actually received. This indicates that standard regimens may not be the most effective for every patient. Discussion and future directions: These results demonstrate that the mathematical model can be predictive of tumor response using data at the earliest times of therapy regimens. The in silico results illustrate how for individual patients (depending on their unique tumor characteristics and vasculature—captured by the calibrated parameters of the model), therapy regimens can be tailored and even optimized (via established optimal control theory methods) to each patient using a mathematical model and simulation studies. The present investigation represents a significant first step towards personalizing patient regimens through quantitative imaging and mathematical modeling. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE Citation Format: Angela M Jarrett, David A Hormuth II, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results [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 P2-16-17.

Introduction: The purpose of this study is to quantify the effects of combination trastuzumab and radiation therapy in HER2+ breast cancer. Breast tumors can become hypoxic which increases radioresistance partly due to the upregulation of hypoxia inducible factors (HIFs). HIF-1α promotes cancer cell survival and has been found to be upregulated by phosphatidylinositol-3 kinase (P13K) signaling. Trastuzumab, a targeted therapy, has been used in combination with radiation in the adjuvant setting to treat HER2+ patients. Trastuzumab causes cell cycle arrest by inhibiting P13K pathways and been shown to increase tumor oxygenation in vivo. There is evidence that trastuzumab sensitizes HER2+ breast cancer to radiation therapy; however, longitudinal studies are limited and fail to quantify sensitization over time, reducing clinical relevance. There are three main goals to this study: 1) quantify the effects of combination trastuzumab and radiation therapy in normoxic and hypoxic conditions in vitro, 2) quantify the regulation of P13K when treated with trastuzumab in hypoxic conditions in vitro, and 3) test the hypothesis that trastuzumab sensitizes HER2+ breast cancer to radiation therapy in an in vivo model of HER2+ breast cancer. Experimental Design: For in vitro studies, BT474-GFP expressing HER2+ breast cancer cells were plated in a 96 well plate. 24 hours later, cell media was changed to simulate hypoxic (50 or 100 μM CoCl2) or normoxic (0 μM CoCl2) conditions and then treated with trastuzumab and/or radiation (various dosing and timing) and imaged using the IncuCyte ZOOM System every 6 hours for 7 days. Cell number was automatically quantified and surviving fractions (SF) were calculated for each time point. An enzyme-linked immunosorbent assay (ELISA) was used to quantify HIF-1α and P13K protein levels in cells. For in vivo studies, 107 BT474 HER2+ breast cancer cells were implanted subcutaneously into athymic nude mice. After reaching 250 mm3, tumors were treated with trastuzumab (10 mg/kg, 2 doses), and/or radiation (5 or 10 Gy, single dose), or saline and were measured with calipers for six weeks. A non-parametric Wilcoxon rank sum test was used to determine statistical differences in tumor size between treatment groups. Degree of synergy over time in combination treatment groups both in vitro and in vivo was determined using a Bliss independence model. Results and Discussion: The SF of cells 5 days after 10 Gy irradiation under hypoxic conditions was significantly higher at 51.2% (50 μM CoCl2, P < 0.05) and 65.5% (100 μM CoCl2, P < 0.01) than normoxic conditions with a SF of 44.8%. Cells treated with 3 μg/ml of trastuzumab 24 hours prior to 10 Gy irradiation had a significant decrease in SF 5 days post irradiation under both normoxic (SF 40.8%) and hypoxic conditions (100 μM CoCl2 - SF 58.8%) compared to either monotherapy (P < 0.05). In vivo, treatment with trastuzumab and radiation results in a faster rate of tumor regression. Mice (n = 4) had a significant (P < 0.05) 45% decrease in tumor size 7 days after 10 Gy irradiation when treated in combination with trastuzumab compared to control (n = 5) or radiation alone (n = 5). Mice treated with radiation alone did not have a significant decrease in tumor size until 14 days after radiation therapy. Conclusion: Quantifying the synergy between trastuzumab and radiation over time will elucidate optimal combination regimens and has the potential to decrease the amount of therapy needed to achieve tumor regression. Systematically evaluating the radiosensitizing effect of trastuzumab in vitro under normoxic and hypoxic conditions may provide mechanistic insight on how to overcome radioresistance in HER2+ breast cancer. We acknowledge the support of CPRIT RR160005, NCI R01CA186193, and ACS RSG-18-006-01-CCE. Citation Format: Meghan Bloom, Jack Virostko, Anna Sorace, Thomas Yankeelov. Quantifying the effects of combination trastuzumab and radiation therapy in HER2 positive breast cancer under normoxic and hypoxic conditions [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 P1-18-25.

Background The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. Methods Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. Results Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p Conclusions Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.