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Ultrasound molecular imaging as a non-invasive companion diagnostic for netrin-1 interference therapy in breast cancer.

ABSTRACT: In ultrasound molecular imaging (USMI), ligand-functionalized microbubbles (MBs) are used to visualize vascular endothelial targets. Netrin-1 is upregulated in 60% of metastatic breast cancers and promotes tumor progression. A novel netrin-1 interference therapy requires the assessment of netrin-1 expression prior to treatment. In this study, we studied netrin-1 as a target for USMI and its potential as a companion diagnostic in breast cancer models. Methods: To verify netrin-1 expression and localization, an in vivo immuno-localization approach was applied, in which anti-netrin-1 antibody was injected into living mice 24 h before tumor collection, and revealed with secondary fluorescent antibody for immunofluorescence analysis. Netrin-1 interactions with the cell surface were studied by flow cytometry. Netrin-1-targeted MBs were prepared using MicroMarker Target-Ready (VisualSonics), and validated in in vitro binding assays in static conditions or in a flow chamber using purified netrin-1 protein or netrin-1-expressing cancer cells. In vivo USMI of netrin-1 was validated in nude mice bearing human netrin-1-positive SKBR7 tumors or weakly netrin-1-expressing MDA-MB-231 tumors using the Vevo 2100 small animal imaging device (VisualSonics). USMI feasibility was further tested in transgenic murine FVB/N Tg(MMTV/PyMT634Mul) (MMTV-PyMT) mammary tumors. Results: Netrin-1 co-localized with endothelial CD31 in netrin-1-positive breast tumors. Netrin-1 binding to the surface of endothelial HUVEC and cancer cells was partially mediated by heparan sulfate proteoglycans. MBs targeted with humanized monoclonal anti-netrin-1 antibody bound to netrin-1-expressing cancer cells in static and dynamic conditions. USMI signal was significantly increased with anti-netrin-1 MBs in human SKBR7 breast tumors and transgenic murine MMTV-PyMT mammary tumors compared to signals recorded with either isotype control MBs or after blocking of netrin-1 with humanized monoclonal anti-netrin-1 antibody. In weakly netrin-1-expressing human tumors and normal mammary glands, no difference in imaging signal was observed with anti-netrin-1- and isotype control MBs. Ex vivo analysis confirmed netrin-1 expression in MMTV-PyMT tumors. Conclusions: These results show that USMI allowed reliable detection of netrin-1 on the endothelium of netrin-1-positive human and murine tumors. Significant differences in USMI signal for netrin-1 reflected the significant differences in netrin-1 mRNA & protein expression observed between different breast tumor models. The imaging approach was non-invasive and safe, and provided the netrin-1 expression status in near real-time. Thus, USMI of netrin-1 has the potential to become a companion diagnostic for the stratification of patients for netrin-1 interference therapy in future clinical trials.

SUBMITTER: Wischhusen J

PROVIDER: S-EPMC6217066 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Ultrasound molecular imaging as a non-invasive companion diagnostic for netrin-1 interference therapy in breast cancer.

Wischhusen Jennifer J Wilson Katheryne E KE Delcros Jean-Guy JG Molina-Peña Rodolfo R Gibert Benjamin B Jiang Shan S Ngo Jacqueline J Goldschneider David D Mehlen Patrick P Willmann Juergen K JK Padilla Frederic F

Theranostics 20181006 18

In ultrasound molecular imaging (USMI), ligand-functionalized microbubbles (MBs) are used to visualize vascular endothelial targets. Netrin-1 is upregulated in 60% of metastatic breast cancers and promotes tumor progression. A novel netrin-1 interference therapy requires the assessment of netrin-1 expression prior to treatment. In this study, we studied netrin-1 as a target for USMI and its potential as a companion diagnostic in breast cancer models. <b>Methods:</b> To verify netrin-1 expression ...[more]

PMID: 30429890

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Project description:BackgroundSclerosing adenosis (SA) is a common proliferative benign lesion without atypia in the breast that may mimic invasive ductal carcinoma (IDC) on medical imaging, leading to it often being misdiagnosed and mistreated. Consequently, the purpose of this study was to assess the diagnostic value of multimodal ultrasound imaging in distinguishing SA from IDC.MethodsMultimodal ultrasound imaging, including automated breast volume scan (ABVS), elasticity imaging (EI), and color Doppler flow imaging (CDFI), were performed on 120 consecutive patients comprising 122 breast lesions (54 SA, 68 IDC). All lesions were pathologically confirmed. Multimodal ultrasound imaging features were compared between the two groups. Binary logistic regression analysis based on ABVS, EI, and CDFI was conducted to formulate a logistic regression equation for differentiating SA from IDC. The diagnostic performances of ABVS, EI, CDFI, and their combination were compared by the receiver operating characteristic (ROC) curve analysis.ResultsThe sensitivity, specificity, and accuracy of ABVS, EI, CDFI, and their combination in differentiating SA from IDC were, respectively, 75.00%, 72.22%, and 73.77%; 86.76%, 72.22%, and 80.33%; 73.53%, 64.81%, and 69.67%; and 88.24%, 74.07%, and 81.97%. Combining multimodal ultrasound imaging yielded an area under the curve (AUC) of 0.895 (95% confidence interval: 0.827-0.943), which was higher than that of ABVS, EI, and CDFI, with AUC values of 0.736, 0.795, and 0.692, respectively, and the difference was statistically significant (ABVS vs. combined model, P<0.001; CDFI vs. combined model, P<0.001; EI vs. combined model, P<0.001). There was no significant difference in the diagnostic efficacy among the three imaging modalities (ABVS vs. EI, P=0.266; ABVS vs. CDFI, P=0.4671; EI vs. CDFI, P=0.051). Compared with those in IDC, the calcification (16.67% vs. 57.35%; P<0.001) and retraction phenomena in the coronal planes (18.52% vs. 57.35%; P<0.001) were less common in patients with SA, while circumscribed margin (38.89% vs. 5.88%; P<0.001), vascularity grade 0-I (64.81% vs. 26.47%; P<0.001), and elasticity scores 1-3 (72.22% vs. 13.24%; P<0.001) were more frequently found in patients with SA. Patients with SA were significantly younger than were patients with IDC (43±11 vs. 54±11 years; P<0.001), and the lesion size was smaller in patients with SA than in those with IDC (median size 1.0 cm; interquartile range (IQR), 0.9 cm vs. median size 1.3 cm; IQR, 1.3 cm; P<0.001).ConclusionsThe preliminary results suggested that multimodal ultrasound imaging can improve the diagnostic accuracy of SA and provide additional information for differential diagnosis of SA and IDC.

Project description:ObjectivesTo determine the diagnostic accuracy of postmortem magnetic resonance imaging (PM-MRI) and postmortem ultrasound (PM-US) for perinatal autopsy in the same patient cohort, and to determine whether PM-US can provide the same anatomical information as PM-MRI.MethodsIn this prospective, 5-year (July 2014-July 2019) single-center study, we performed 1.5-T PM-MRI and PM-US in an unselected cohort of perinatal deaths. The diagnostic accuracies of both modalities were calculated, using autopsy as the reference standard. As a secondary objective, the concordance rates between the two imaging modalities for the overall main diagnosis and for five anatomical regions (brain, spine, thorax, heart and abdomen) were calculated.ResultsDuring the study period, 136 cases underwent both PM-US and PM-MRI, of which 88 (64.7%) also underwent autopsy. There was no significant difference in the rates of concordance with autopsy between the two modalities for overall diagnosis (PM-US, 86.4% (95% CI, 77.7-92.0%) vs PM-MRI, 88.6% (95% CI, 80.3-93.7%)) or in the sensitivities and specificities for individual anatomical regions. There were more non-diagnostic PM-US than PM-MRI examinations for the brain (22.8% vs 3.7%) and heart (14.7% vs 5.1%). If an 'imaging-only' autopsy had been performed, PM-US would have achieved the same diagnosis as 1.5-T PM-MRI in 86.8% (95% CI, 80.0-91.5%) of cases, with the highest rates of agreement being for spine (99.3% (95% CI, 95.9-99.9%)) and cardiac (97.3% (95% CI, 92.4-99.1%)) findings and the lowest being for brain diagnoses (85.2% (95% CI, 76.9-90.8%)).ConclusionAlthough there were fewer non-diagnostic cases using PM-MRI than for PM-US, the high concordance rate for overall diagnosis suggests that PM-US could be used for triaging cases when PM-MRI access is limited or unavailable. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Project description:Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient's likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.

Project description:BackgroundEarly studies have demonstrated the potential of deep learning in bringing revolutionary changes in medical analysis. However, it is unknown which deep learning based diagnostic pattern is more effective for differentiating malignant and benign breast lesions (BLs) and can assist radiologists to reduce unnecessary biopsies.MethodsA total of 506 malignant BLs and 557 benign BLs were enrolled in this study after excluding incomplete ultrasound images. 396 malignant BLs and 447 benign BLs were included in the training cohort while 110 malignant and 110 benign BLs were included in the validation cohort. All BLs in the training and validation cohort were biopsy-proven. The most common convolutional neural networks (VGG-16 and VGG-19) were applied to identify malignant and benign BLs using grey-scale ultrasound images. Two radiologists determined the malignant (suggestion for biopsy) and benign (suggestion for follow-up) BLs with a 2-step reading session. The first step was based on conventional ultrasound (US) images alone to make a biopsy or follow-up decision. The second step was to take deep learning results into account for the decision adjustment. If a deep learning result of a first-classified benign BL was above the cut-off value, then it was re-classified as malignant.ResultsIn terms of area under the curve (AUC), the VGG-19 model yielded the best diagnostic performance in both training [0.939, 95% confidence interval (CI): 0.924-0.954] and testing dataset (0.959, 95% CI: 0.937-0.982). With the aid of deep learning models, the AUC of radiologists improved from 0.805 (95% CI: 0.744-0.865) to 0.827 (95% CI: 0.771-0.875, VGG-16) and 0.914 (95% CI: 0.871-0.957, VGG-19). The unnecessary biopsies decreased from 10.0% (11/110) to 8.2% (9/110) (assisted by VGG-16) and 0.9% (1/110) (assisted by VGG-19).ConclusionsThe application of deep learning patterns in breast US may improve the diagnostic performance of radiologists by offering a second opinion. And thus, the assist of deep learning algorithm can considerably reduce the unnecessary biopsy rate in the clinical management of breast lesions.

Dataset Information

Ultrasound molecular imaging as a non-invasive companion diagnostic for netrin-1 interference therapy in breast cancer.

Publications

Ultrasound molecular imaging as a non-invasive companion diagnostic for netrin-1 interference therapy in breast cancer.

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