Purpose: Therapeutic nanoparticles are designed to deliver their drug payloads through enhanced permeability and retention (EPR) in solid tumors. The extent of EPR and its variability in human tumors is highly debated and has been proposed as an explanation for variable responses to therapeutic nanoparticles in clinical studies. Experimental Design: We assessed the EPR effect in patients using a 64Cu-labeled nanoparticle, 64Cu-MM-302 (64Cu-labeled HER2-targeted PEGylated liposomal doxorubicin), and imaging by Positron Emission Tomography/Computed Tomography (PET/CT). Nineteen patients with HER2-positive metastatic breast cancer underwent 2–3 PET/CT scans post-administration of 64Cu-MM-302 as part of a clinical trial of MM-302 plus trastuzumab with and without cyclophosphamide (). Results: Significant background uptake of 64Cu-MM-302 was observed in liver and spleen. Tumor accumulation of 64Cu-MM-302 at 24–48 h varied 35-fold (0.52 to 18.5 %ID/kg) including deposition in bone and brain lesions, and was independent of systemic plasma exposure. Computational analysis quantified rates of deposition and washout, indicating peak liposome deposition at 24–48 h. Patients were classified based on 64Cu-MM-302 lesion deposition using a cut-point that is comparable to a response threshold in preclinical studies. In a retrospective exploratory analysis of patient outcomes relating to drug levels in tumor lesions, high 64Cu-MM-302 deposition was associated with more favorable treatment outcomes (hazard ratio = 0.42). Conclusions: These findings provide important evidence and quantification of the EPR effect in human metastatic tumors, and support imaging nanoparticle deposition in tumors as a potential means to identify patients well-suited for treatment with therapeutic nanoparticles.

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Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

In designing co-clinical cancer studies, preclinical imaging brings unique challenges that emphasize the gap between man and mouse. Our group is developing quantitative imaging methods for the preclinical arm of a co-clinical trial studying immunotherapy and radiotherapy in a soft tissue sarcoma model. In line with treatment for patients enrolled in the clinical trial SU2C-SARC032, primary mouse sarcomas are imaged with multi-contrast micro-MRI (T1 weighted, T2 weighted, and T1 with contrast) before and after immune checkpoint inhibition and pre-operative radiation therapy. Similar to the patients, after surgery the mice will be screened for lung metastases with micro-CT using respiratory gating. A systems evaluation was undertaken to establish a quantitative baseline for both the MR and micro-CT systems against which others systems might be compared. We have constructed imaging protocols which provide clinically-relevant resolution and contrast in a genetically engineered mouse model of sarcoma. We have employed tools in 3D Slicer for semi-automated segmentation of both MR and micro-CT images to measure tumor volumes efficiently and reliably in a large number of animals. Assessment of tumor burden in the resulting images was precise, repeatable, and reproducible. Furthermore, we have implemented a publicly accessible platform for sharing imaging data collected during the study, as well as protocols, supporting information, and data analyses. In doing so, we aim to improve the clinical relevance of small animal imaging and begin establishing standards for preclinical imaging of tumors from the perspective of a co-clinical trial.

Small animal imaging has become essential in evaluating new cancer therapies as they are translated from the preclinical to clinical domain. However, preclinical imaging faces unique challenges that emphasize the gap between mouse and man. One example is the difference in breathing patterns and breath-holding ability, which can dramatically affect tumor burden assessment in lung tissue. As part of a co-clinical trial studying immunotherapy and radiotherapy in sarcomas, we are using micro-CT of the lungs to detect and measure metastases as a metric of disease progression. To effectively utilize metastatic disease detection as a metric of progression, we have addressed the impact of respiratory gating during micro-CT acquisition on improving lung tumor detection and volume quantitation. Accuracy and precision of lung tumor measurements with and without respiratory gating were studied by performing experiments with in vivo images, simulations, and a pocket phantom. When performing test-retest studies in vivo, the variance in volume calculations was 5.9% in gated images and 15.8% in non-gated images, compared to 2.9% in post-mortem images. Sensitivity of detection was examined in images with simulated tumors, demonstrating that reliable sensitivity (true positive rate (TPR) � 90%) was achievable down to 1.0 mm 3 lesions with respiratory gating, but was limited to � 8.0 mm 3 in non-gated images. Finally, a clinically-inspired "pocket phantom" was used during in vivo mouse scanning to aid in refining and assessing the gating protocols. Application of respiratory gating techniques reduced variance of repeated volume measurements and significantly improved the accuracy of tumor volume quantitation in vivo.

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Liposomes (LP) deliver drug to tumors due to enhanced permeability and retention (EPR). LP were labeled with 64Cu for positron emission tomography (PET) to image tumor localization. Bevacizumab (bev), a VEGF targeted antibody, may modify LP delivery by altering tumor EPR and this change can also be imaged.Objective: Assess the utility of 64Cu-labeled LP for PET in measuring altered LP delivery early after treatment with bev.Methods: HT-29 human colorectal adenocarcinoma tumors were grown subcutaneously in SCID mice. Empty LP MM-DX-929 (Merrimack Pharmaceuticals, Inc. Cambridge, MA) were labeled with 64CuCl2 chelated with 4-DEAP-ATSC. Tumor-bearing mice received ~200-300 μCi of 64Cu-MM-DX-929 and imaged with microPET. All mice were scanned before and after the treatment period, in which half of the mice received bev for one week. Scans were compared for changes in LP accumulation during this time. Initially, tissues were collected after the second PET for biodistribution measurements and histological analysis. Subsequent groups were divided for further treatment. Tumor growth following bev treatment, with or without LP-I, was assessed compared to untreated controls.Results: PET scans of untreated mice showed increased uptake of 64Cu-MM-DX-929, with a mean change in tumor SUVmax of 43.9%±6.6% (n=10) after 7 days. Conversely, images of treated mice showed that liposome delivery did not increase, with changes in SUVmax of 7.6%±4.8% (n=12). Changes in tumor SUVmax were significantly different between both groups (p=0.0003). Histology of tumor tissues indicated that short-term bev was able to alter vessel size. Therapeutically, while bev monotherapy, LP-I monotherapy, and treatment with bev followed by LP-I all slowed HT-29 tumor growth compared to controls, combination provided no therapeutic benefit.Conclusions: PET with tracer LP 64Cu-MM-DX-929 can detect significant differences in LP delivery to colon tumors treated with bev when compared to untreated controls. Imaging with 64Cu-MM-DX-929 is sensitive enough to measure drug-induced changes in LP localization which can have an effect on outcomes of treatment with LP.