Bridging the translational gap: Implementation of multimodal small animal imaging strategies for tumor burden assessment in a co-clinical trial

Patient-derived tumor models in cancer research: Evaluation of the oncostatic effects of melatonin

The development of new anticancer therapies tends to be very slow. Although their impact on potential candidates is confirmed in preclinical studies, ∼95 % of these new therapies are not approved when tested in clinical trials. One of the main reasons for this is the lack of accurate preclinical models. In this context, there are different patient-derived models, which have emerged as a powerful oncological tool: patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived cells (PDCs). Although all these models are widely applied, PDXs, which are created by engraftment of patient tumor tissues into mice, is considered more reliable. In fundamental research, the PDX model is used to evaluate drug-sensitive markers and, in clinical practice, to select a personalized therapeutic strategy. Melatonin is of particular importance in the development of innovative cancer treatments due to its oncostatic impact and lack of adverse effects. However, the literature regarding the oncostatic effect of melatonin in patient-derived tumor models is scant. This review aims to describe the important role of patient-derived models in the development of anticancer treatments, focusing, in particular, on PDX models, as well as their use in cancer research. This review also summarizes the existing literature on the anti-tumoral effect of melatonin in patient-derived models in order to propose future anti-neoplastic clinical applications.

The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings

Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members.

Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma

This study aims to investigate whether adding neoadjuvant radiotherapy (RT), anti-programmed cell death protein-1 (PD-1) antibody (anti-PD-1), or RT + anti-PD-1 to surgical resection improves disease-free survival for mice with soft tissue sarcomas (STS). We generated a high mutational load primary mouse model of STS by intramuscular injection of adenovirus expressing Cas9 and guide RNA targeting Trp53 and intramuscular injection of 3-methylcholanthrene (MCA) into the gastrocnemius muscle of wild-type mice (p53/MCA model). We randomized tumor-bearing mice to receive isotype control or anti-PD-1 antibody with or without radiotherapy (20 Gy), followed by hind limb amputation. We used micro-CT to detect lung metastases with high spatial resolution, which was confirmed by histology. We investigated whether sarcoma metastasis was regulated by immunosurveillance by lymphocytes or tumor cell-intrinsic mechanisms. Compared with surgery with isotype control antibody, the combination of anti-PD-1, radiotherapy, and surgery improved local recurrence-free survival (P = 0.035) and disease-free survival (P = 0.005), but not metastasis-free survival. Mice treated with radiotherapy, but not anti-PD-1, showed significantly improved local recurrence-free survival and metastasis-free survival over surgery alone (P = 0.043 and P = 0.007, respectively). The overall metastasis rate was low (∼12%) in the p53/MCA sarcoma model, which limited the power to detect further improvement in metastasis-free survival with addition of anti-PD-1 therapy. Tail vein injections of sarcoma cells into immunocompetent mice suggested that impaired metastasis was due to inability of sarcoma cells to grow in the lungs rather than a consequence of immunosurveillance. In conclusion, neoadjuvant radiotherapy improves metastasis-free survival after surgery in a primary model of STS.

Automated segmentation of lungs and lung tumors in mouse micro-CT scans

Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.

Current research developments of patient-derived tumour xenograft models (Review)

Patient-derived tumor xenograft (PDTX) models are established by transferring patient tumors into immunodeficient mice. In these murine models, the characteristics of the primary tumor are retained, including the microenvironment of tumor cell growth and histopathology. Due to this, it has become the most reliable in vivo human cancer model. However, the success rates differ by type of tumor, site of transplantation and tumor aggressiveness. Subcutaneous transplantation is a standard method for PDTX, and subrenal capsule transplantation improves the engraftment rate. Recently, PDTX models are frequently used in the fields of precision medicine, predictive biomarkers, evaluation of drug efficacy and preclinical research on tumor immunotherapeutic drugs. The aim of the present article was to review the establishment, clinical applications and limitations of the PDTX model in tumor research.

Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76-0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

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.

Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas

Purpose To establish a platform for quantitative tissue-based interpretation of cytoarchitecture features from tumor MRI measurements. Materials and Methods In a pilot preclinical study, multicontrast in vivo MRI of murine soft-tissue sarcomas in 10 mice, followed by ex vivo MRI of fixed tissues (termed MR histology), was performed. Paraffin-embedded limb cross-sections were stained with hematoxylin-eosin, digitized, and registered with MRI. Registration was assessed by using binarized tumor maps and Dice similarity coefficients (DSCs). Quantitative cytometric feature maps from histologic slides were derived by using nuclear segmentation and compared with registered MRI, including apparent diffusion coefficients and transverse relaxation times as affected by magnetic field heterogeneity (T2* maps). Cytometric features were compared with each MR image individually by using simple linear regression analysis to identify the features of interest, and the goodness of fit was assessed on the basis of R² values. Results Registration of MR images to histopathologic slide images resulted in mean DSCs of 0.912 for ex vivo MR histology and 0.881 for in vivo MRI. Triplicate repeats showed high registration repeatability (mean DSC, >0.9). Whole-slide nuclear segmentations were automated to detect nuclei on histopathologic slides (DSC = 0.8), and feature maps were generated for correlative analysis with MR images. Notable trends were observed between cell density and in vivo apparent diffusion coefficients (best line fit: R² = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R² = 0.99, P < .001) and variance in nuclear circularity (best line fit: R² = 0.98, P < .001). Conclusion An infrastructure for registering and quantitatively comparing in vivo tumor MRI with traditional histologic analysis was successfully implemented in a preclinical pilot study of soft-tissue sarcomas. Keywords: MRI, Pathology, Animal Studies, Tissue Characterization Supplemental material is available for this article. © RSNA, 2021.

Touchscreen cognitive testing: Cross-species translation and co-clinical trials in neurodegenerative and neuropsychiatric disease

Translating results from pre-clinical animal studies to successful human clinical trials in neurodegenerative and neuropsychiatric disease presents a significant challenge. While this issue is clearly multifaceted, the lack of reproducibility and poor translational validity of many paradigms used to assess cognition in animal models are central contributors to this challenge. Computer-automated cognitive test batteries have the potential to substantially improve translation between pre-clinical studies and clinical trials by increasing both reproducibility and translational validity. Given the structured nature of data output, computer-automated tests also lend themselves to increased data sharing and other open science good practices. Over the past two decades, computer automated, touchscreen-based cognitive testing methods have been developed for non-human primate and rodent models. These automated methods lend themselves to increased standardization, hence reproducibility, and have become increasingly important for the elucidation of the neurobiological basis of cognition in animal models. More recently, there have been increased efforts to use these methods to enhance translational validity by developing task batteries that are nearly identical across different species via forward (i.e., translating animal tasks to humans) and reverse (i.e., translating human tasks to animals) translation. An additional benefit of the touchscreen approach is that a cross-species cognitive test battery makes it possible to implement co-clinical trials-an approach developed initially in cancer research-for novel treatments for neurodegenerative disorders. Co-clinical trials bring together pre-clinical and early clinical studies, which facilitates testing of novel treatments in mouse models with underlying genetic or other changes, and can help to stratify patients on the basis of genetic, molecular, or cognitive criteria. This approach can help to determine which patients should be enrolled in specific clinical trials and can facilitate repositioning and/or repurposing of previously approved drugs. This has the potential to mitigate the resources required to study treatment responses in large numbers of human patients.

Effect of Panax notoginseng Saponins on Focal Cerebral Ischemia-Reperfusion in Rat Models: A Meta-Analysis

With the increase of the aging population, the high mortality and disability rates caused by ischemic stroke are some of the major problems facing the world, and they dramatically burden the society. Panax notoginseng (Burk) F. H. Chen, a traditional Chinese medicine, is commonly used for promoting blood circulation and removing blood stasis, and its main bioactive components are Panax notoginseng saponins (PNS). Therefore, we performed a meta-analysis on focal cerebral ischemia-reperfusion animal models established with middle cerebral artery occlusion (MCAO) surgery to evaluate the therapeutic effect of PNS. We systematically searched the reports of PNS in MCAO animal experiments in seven databases. We assessed the study quality using two literature quality evaluation criteria; evaluated the efficacy of PNS treatment based on the outcomes of the neurological deficit score (NDS), cerebral infarct volume (CIV), and biochemical indicators via a random/fixed-effects model; and performed a subgroup analysis utilizing ischemia duration, drug dosage, intervention time, and administration duration. We also compared the efficacy of PNS with positive control drugs or combination treatment. As a result, we selected 14 eligible studies from the 3,581 searched publications based on the predefined exclusion-inclusion criteria. PNS were significantly associated with reduced NDS, reduced CIV, and inhibited release of the inflammatory factors IL-1β and TNF-α in the focal MCAO rat models. The PNS combination therapy outperformed the PNS alone. In addition, ischemia time, drug dosage, intervention time, and administration duration in the rat models all had significant effects on the efficacy of PNS. Although more high-quality studies are needed to further determine the clinical efficacy and guiding parameters of PNS, our results also confirmed that PNS significantly relieves the focal cerebral ischemia-reperfusion in rat models. In the animal trials, it was suggested that an early intervention had significant efficacy with PNS alone or PNS combination treatment at a dosage lower than 25 mg/kg or 100–150 mg/kg for 4 days or longer. These findings further guide the therapeutic strategy for clinical cerebral ischemic stroke.

MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice

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 impact of respiratory gating on improving volume measurement of murine lung tumors in micro-CT imaging

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 mm3 lesions with respiratory gating, but was limited to ≥ 8.0 mm3 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.