Matthew R. Lowerison

Compound speckle model detects anti‐angiogenic tumor response in preclinical nonlinear contrast‐enhanced ultrasonography

Purpose: This paper proposes a method for analyzing the first‐order speckle statistics of nonlinear contrast‐enhanced ultrasound images from tumors. Methods: Contrast signal intensity is modeled as a compound distribution of exponential probability density functions with a gamma weighting function. The gamma probability weighting function serves as an approximation for log‐normally distributed flow velocities in a vascular network. The model was applied to sub‐harmonic bolus‐injection images acquired from a mouse breast cancer xenograft model treated with murine version bevacizumab. Results: The area under curve produced using the compound statistical model could more accurately discriminate anti‐VEGF‐treated tumors from untreated tumors than conventional contrast‐enhanced ultrasound image processing. This result was validated with gold standard histological measures of microvascular density. Fractal vessel geometry was estimated using the gamma weighting function and tested against micro‐CT perfusion casting. Treated tumors had a significantly lower vascular fractal dimension than control tumors. Vascular complexity estimated using the ultrasound compound statistical model performed similarly to micro‐CT fractal dimension for discriminating treated from control tumors. Conclusion: The proposed technique can quantify tumor perfusion and provide an index of vascular complexity, making it a potentially useful addition for clinical detection of vascular normalization in anti‐angiogenic trials.

Improved Linear Contrast-Enhanced Ultrasound Imaging via Analysis of First-Order Speckle Statistics

The linear subtraction methods commonly used for preclinical contrast-enhanced imaging are susceptible to registration errors and motion artifacts that lead to reduced contrast-to-tissue ratios. To address this limitation, a new approach to linear contrast-enhanced ultrasound (CEUS) is proposed based on the analysis of the temporal dynamics of the speckle statistics during wash-in of a bolus injection of microbubbles. In the proposed method, the speckle signal is approximated as a mixture of temporally varying random processes, representing the microbubble signal, superimposed onto spatially heterogeneous tissue backscatter in multiple subvolumes within the region of interest. A wash-in curve is constructed by plotting the effective degrees of freedom (EDoFs) of the histogram of the speckle signal as a function of time. The proposed method is, therefore, named the EDoF method. The EDoF parameter is proportional to the shape parameter of the Nakagami distribution. Images acquired at 18 MHz from a murine mammary fat pad breast cancer xenograft model were processed using gold-standard nonlinear amplitude modulation, conventional linear subtraction, and the proposed statistical method. The EDoF method shows promise for improving the robustness of linear CEUS based on reduced frame-to-frame variability compared with the conventional linear subtraction time-intensity curves. Wash-in curve parameters estimated using the EDoF method also demonstrate higher correlation to nonlinear CEUS than the conventional linear method. The conceptual basis of the statistical method implies that EDoF wash-in curves may carry information about vascular complexity that could provide valuable new imaging biomarkers for cancer research.

A compound speckle model for vascular complexity quantification in nonlinear contrast-enhanced ultrasonography

A statistical method of contrast-enhanced ultrasound image analysis is introduced that models contrast speckle as a compound distribution of exponential probability density functions. The model, when applied to nonlinear destruction-reperfusion images acquired from a murine breast cancer xenograft model, generates a weighting function that is shown to distinguish between different vascular environments. Plotting the change in this weighting function over the course of microbubble reperfusion yields wash-in curves consistent with the expected mono-exponential kinematics. The proposed statistical method quantifies heterogeneities in contrast speckle from the tumor model and may provide useful new metrics for vascular characterization of tumors.

A two-component speckle model for detection of microbubble signals in linear contrast-enhanced ultrasonography

Contrast-enhanced ultrasound (CEUS) serves oncology by imaging tumor blood supply to enable quantification of longitudinal vascular changes and monitoring of treatment responses. Unfortunately, the linear subtraction methods commonly used for preclinical imaging are susceptible to registration errors and motion artifacts that lead to reduced contrast-to-tissue ratios. In this presentation, an alternative approach is proposed to improve discrimination between the contrast and tissue signals by comparing the first-order speckle statistics of images acquired before and after injection of microbubbles. The microbubble signal component is modeled as a temporally varying random process superimposed on a Rayleigh-distributed speckle signal representing backscatter from tissue. Images were acquired at 18 MHz from a murine orthotopic (mammary fat pad) xenograft breast cancer model following a bolus injection of microbubbles. Images were processed using gold-standard pulse inversion (nonlinear CEUS), conventional l...

Compound speckle model demonstrates two-phase wash-in of contrast-enhanced ultrasound cine loops

During contrast-enhanced ultrasound (CEUS) imaging of tumor microvascular perfusion, nearby arteriole enhancement can be a dominant feature of the wash-in kinetics that obscures the perfusion characteristics of the capillary bed. This presentation demonstrates that statistical wash-in curves generated using our compound CEUS speckle model1 exhibit two distinct phases corresponding to “fast-flow” and “slow-flow” enhancement that can be detected by fitting a linear combination of two monoexponential functions with different time constants. CEUS cine loops were acquired from a patient-derived xenograft model of renal cell carcinoma, where fresh tumor fragments were engrafted into the chicken embryo chorioallantoic membrane. Subharmonic CEUS images were acquired at 18 MHz using a destruction-replenishment protocol. Enhancement of manually segmented tumor cross-sections was analyzed offline in MATLAB. The CEUS cine loops from this xenograft model frequently exhibited in-plane arteriole enhancement. The two-pha...

Reduced variability of CEUS perfusion estimates in a patient-derived xenograft model via analysis of speckle statistics

Contrast-enhanced ultrasound (CEUS) permits quantification and monitoring of tumor vascular changes in response to anti-angiogenic treatment with the goal of informing targeted therapy. Conventional mean-intensity-based CEUS analysis discounts additional information that may be available from the first-order speckle statistics in a CEUS image. We demonstrate that our compound speckle model for analysis of CEUS images reduces the variability in tumor perfusion quantification, particularly for estimates of blood volume, and reduces the sensitive/resistant classification ambiguity caused by heterogeneous tumor samples in a patient-derived xenograft model of renal cell carcinoma.

Reduced variability of contrast-enhanced ultrasound perfusion estimates in a patient-derived xenograft model via analysis of speckle statistics

Contrast-enhanced ultrasound (CEUS) permits quantification and monitoring of tumor vascular changes in response to anti-angiogenic treatment with the goal of informing targeted therapy. Conventional mean-intensity-based CEUS analysis discounts additional information that may be available from the first-order speckle statistics in a CEUS image. In this presentation, we demonstrate that our compound speckle model for analysis of CEUS images [1] reduces the variability in tumor perfusion quantification, particularly for estimates of blood volume, and reduces the sensitive/resistant classification ambiguity caused by heterogeneous tumor samples. This analysis technique has been previously applied to a mouse xenograft model of breast cancer [1], where it significantly improved the classification accuracy between control and bevacizumab-treated tumors over conventional CEUS analysis.

PD04-12 CLINICAL CORRELATION OF PATIENT-DERIVED XENOGRAFT MODEL USING THE EX-OVO AVIAN EMBRYO TO PREDICT TARGETED THERAPY TUMOR RESISTANCE IN RENAL CELL CARCINOMA

INTRODUCTION AND OBJECTIVES: Cytoreductive nephrectomy (CN) remains a mainstay in the treatment of metastatic renal cell carcinoma (mRCC). Prior literature has shown around 2/3rds of patients do not receive timely systemic therapy (ST) after CN, however there is limited understanding of how a delay affects progression and survival. Our aim was to identify whether a delay of the initiation of ST was associated with worse overall survival as well as to further characterize the reasons for a delay. METHODS: From an institutional database of 2,906 patients surgically treated for renal masses between 2005 and 2016, we identified 70 patients who underwent CN for mRCC and who were initiated on ST in the adjuvant setting. Cox regression analysis was used to evaluate whether delays in systemic therapy > 3 months and > 6 months were predictive of worse overall survival. RESULTS: Of the 70 patients, the majority had a favorable ECOG performance status (90% ECOG 0-1, 10% ECOG 2-3) while only 3 patients had brain metastasis at time of CN. Median age at diagnosis was 60 years. Our cohort had a 2-year overall survival of 60.8% from diagnosis and 49.4% after initiation of ST with a median follow-up of 27.1 months. Median time to ST after CN was 3 months (IQR 1.536.77). 94.2% of patients received targeted therapy while the remainder were treated with IL-2. Delays in initiating ST after CN were not associated with worse overall survival > 3 months after CN (HR 1⁄4 0.64, p1⁄4 0.387, 95% CI 0.24-1.75) and from 3 to <6 months (HR 1⁄4 0.5, p 1⁄4 0.208, 95% CI 0.17-1.47). Interestingly, delays in ST > 6 months were associated with improved survival (HR 1⁄4 0.19, p1⁄40.017, 95% CI 0.740.017). Of the patients who experienced unintended delays, 42.3% were awaiting a clinical trial, 30.8% experienced delayed ST due to patient preference or poor follow-up, and 19.2% had a complication from surgical therapy. CONCLUSIONS: A delay in the initiation of ST in patients with mRCC after CN did not appear to be associated with worse overall survival. The improved survival in patients who initiated ST > 6 months and trend towards improved survival at > 3 months after CN is likely related to an immortal time bias. Ongoing randomized controlled trials may provide more evidence regarding the optimal timing of ST after CN and the clinical implications of a delay in ST.

Repurposing Albendazole: new potential as a chemotherapeutic agent with preferential activity against HPV-negative head and neck squamous cell cancer

Albendazole is an anti-helminthic drug that has been shown to exhibit anti-cancer properties, however its activity in head and neck squamous cell cancer (HNSCC) was unknown. Using a series of in vitro assays, we assessed the ability of albendazole to inhibit proliferation in 20 HNSCC cell lines across a range of albendazole doses (1 nM–10 μM). Cell lines that responded to treatment were further examined for cell death, inhibition of migration and cell cycle arrest. Thirteen of fourteen human papillomavirus-negative HNSCC cell lines responded to albendazole, with an average IC50 of 152 nM. In contrast, only 3 of 6 human papillomavirus-positive HNSCC cell lines responded. Albendazole treatment resulted in apoptosis, inhibition of cell migration, cell cycle arrest in the G2/M phase and altered tubulin distribution. Normal control cells were not measurably affected by any dose tested. This study indicates that albendazole acts to inhibit the proliferation of human papillomavirus-negative HNSCC cell lines and thus warrants further study as a potential chemotherapeutic agent for patients suffering from head and neck cancer.

Abstract 2137: JAK2 as a novel therapeutic target in anaplastic thyroid cancer

Introduction: Thyroid carcinoma is the most common endocrine malignancy. Anaplastic thyroid cancer (ATC) is rare (1.3%) and represents arguably the most lethal human malignancy with 1-year survival rates of only 10%. There are currently no effective treatments for the majority of patients, highlighting an urgent need for novel therapeutics to manage this disease. Objective: To validate the functional importance of JAK2 as a therapeutic target in ATC. Methods: In this study, we have used siRNA knockdown to interrogate the JAK2 signaling pathway as a therapeutic target in ATC. We investigated the mechanism of action and cell death, and assayed for migration and invasion, in vitro. The chick chorioallantoic membrane (CAM) model was also utilized for drug testing, whereby 1x106 Cal62 ATC cells were on-planted to the chick embryo membrane. Two days post on-plant, CAM models were treated with the vehicle (DMSO) or lestaurtinib to measure outcomes including tumor volume and vascularity. Results: We identified the JAK2 inhibitor lestaurtinib as an inhibitory agent controlling cell line proliferation at submicromolar mean inhibitory concentrations. Immunoblotting revealed the inhibition of phosphorylation of the downstream signaling molecule STAT5 in a dose-dependent manner. Treatment of Cal62 cells resulted in a decrease in cell migration using the scratch-wound assay. The anti-proliferative effective of lestaurtinib did not cause apoptosis, autophagy or cell senescence. CAM models treated with a 4 uM dose of lestaurtinib showed a significant decrease in both tumor volume and vascularity. Conclusions: Lestaurtinib was found to provide potent control of ATC cell proliferation and migration, and was also found to decrease tumor growth and vascularity in a CAM model. Knockout studies are underway to confirm that the anticancer effect of this drug is indeed mediated through JAK2 signaling. If validated, JAK2 represents a novel therapeutic target for the treatment of aggressive thyroid cancers. Citation Format: Nicole C. Pinto, Kara Ruicci, Stephenie Prokopec, Karlee Searle, Matthew Lowerison, John Yoo, Kevin Fung, Danielle MacNeil, Hon S. Leong, Alessandro Datti, Paul C. Boutros, John W. Barrett, Anthony C. Nichols. JAK2 as a novel therapeutic target in anaplastic thyroid cancer [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 2137. doi:10.1158/1538-7445.AM2017-2137