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Novel therapeutic approaches are needed for patients with glioblastoma (GBM) who otherwise have limited options. Here we studied and deployed non-freezing ‘cytostatic’ hypothermia to stunt GBM growth. This contrasts with ablative, cryogenic hypothermia: a double-edged sword against tumors infiltrating otherwise healthy tissue. We investigated three grades of hypothermia in vitro and identified a cytostatic window of 20–25°C. For some glioma lines, 18 h/d of cytostatic hypothermia was sufficient to halt division in vitro. Cytostatic hypothermia induced cell cycle arrest, reduced metabolite production and consumption, and reduced inflammatory cytokine synthesis. Next, we fabricated an experimental device to test local cytostatic hypothermia in vivo in two rodent models of GBM: utilizing the rat F98 and the human U-87 MG lines. Hypothermia more than doubled the median survival of F98 bearing rats from 3.9 weeks to 9.7 weeks and two rats survived through 12 weeks. All U-87 MG bearing rats that successfully received cytostatic hypothermia survived their study period. Thus, this approach lengthened survival without chemical interventions. Unlike targeted therapeutics that are successful in preclinical models but fail in clinical trials, cytostatic hypothermia affects multiple cellular processes simultaneously. This, alongside reduced cellular division, suggests that opportunities for tumor evolution are reduced and the likelihood of translation to larger species may be more likely. In addition, based on our work, designs, and the literature, engineering a patient-centric device is tangible. Taken together, cytostatic hypothermia could be a novel approach to cancer therapy and eventually serve a valuable role to patients with GBM.One Sentence SummaryHypothermia influences multiple cellular pathways, can be a safe and effective approach to halt glioblastoma growth, and holds translational promise.

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.

Radiomics provide an exciting approach to developing imaging biomarkers in the context of precision medicine. We focus on the preclinical arm of a co-clinical trial investigating synergy of immunotherapy combined with radiation therapy (RT) and surgical resection using a genetically engineered mouse model of sarcoma. Our protocol involves the acquisition of MRI data with T1, T2 and T1 with contrast agent. There are two MRI time points i.e. one day before RT (20Gy) and one week later. After the second MRI acquisition the primary tumor is surgically removed, and the mice are followed for up to 6 months to investigate for local recurrence or distant metastases. The tumor images are segmented using deep learning. We performed radiomics for the tumor, peritumoral rim and the combined tumor and peritumoral rim. Our first radiomics analysis was focused on determining features which are most indicative to the effects of RT. Our second analysis aimed to answer if radiomics features could predict primary tumor recurrence within this population. Top features were selected for training classifiers based on neural networks and support vector machines. Our results show that gray level radiomic features show that tumors often acquire more heterogeneous texture and that tumor volume increases one-week post RT. The results also suggest that radiomics features serve to indicate likelihood of primary tumor recurrence with the best predictive power in the combined tumor and peritumoral area in pre-RT data (AUC: 0.83). In conclusion, we have created a radiomics pipeline to serve in our current preclinical arm of the 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 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.

Small animal imaging has become essential in evaluating new cancer therapies as they are translated from the preclinical to clinical domain. However, preclinical imaging is faced with 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. Our group is developing quantitative imaging methods for the preclinical arm of a co-clinical trial studying synergy between immunotherapy (anti-PD-1) and radiotherapy in a soft tissue sarcoma model. To mimic imaging performed in patients, primary sarcomas lesions are imaged with micro-MRI, while detection of lung metastases is performed with micro-CT. This study addresses whether respiratory gating during micro-CT acquisition improves lung tumor volume quantitation. Accuracy and precision of lung tumor measurements was determined by performing experiments involving simulations, a pocket phantom and in vivo scans with and without prospective respiratory gating. Sensitivity and precision of segmentation with and without gating was studied using simulated lung tumors. A clinically-inspired “pocket phantom” was used during in vivo mouse scanning to aid in refining and assessing the gating protocols. Finally, we performed a series of in vivo scans on tumor-bearing mice while varying the animal’s position (test-retest), and performing the analyses in triplicate to assess the effects of gating. Application of respiratory gating techniques reduced variance of repeated volume measurements and significantly improved the accuracy of tumor volume quantitation in vivo.

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.