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Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. In this study, we utilize automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin-stained slides of HNSCC tumors (N = 49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.

The tumor microenvironment (TME) plays an important role in the progression of head and neck squamous cell carcinoma (HNSCC). Currently, pathologic assessment of TME is nonstandardized and subject to observer bias. Genome-wide transcriptomic approaches to understanding the TME, while less subject to bias, are expensive and not currently a part of the standard of care for HNSCC. To identify pathology-based biomarkers that correlate with genomic and transcriptomic signatures of TME in HNSCC, cytometric feature maps were generated in a publicly available data set from a cohort of patients with HNSCC, including whole-slide tissue images and genomic and transcriptomic phenotyping (N = 49). Cytometric feature maps were generated based on whole-slide nuclear detection, using a deep-learning algorithm trained for StarDist nuclear segmentation. Cytometric features in each patient were compared to transcriptomic measurements, including Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) scores and stemness scores. With correction for multiple comparisons, one feature (nuclear circularity) demonstrated a significant linear correlation with ESTIMATE stromal score. Two features (nuclear maximum and minimum diameter) correlated significantly with ESTIMATE immune score. Three features (nuclear solidity, nuclear minimum diameter, and nuclear circularity) correlated significantly with transcriptomic stemness score. This study provides preliminary evidence that observer-independent, automated tissue-slide analysis can provide insights into the HNSCC TME which correlate with genomic and transcriptomic assessments.

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 PET/CT. Nineteen patients with HER2-positive metastatic breast cancer underwent 2 to 3 PET/CT scans postadministration of 64Cu-MM-302 as part of a clinical trial of MM-302 plus trastuzumab with and without cyclophosphamide (NCT01304797).Results: Significant background uptake of 64Cu-MM-302 was observed in liver and spleen. Tumor accumulation of 64Cu-MM-302 at 24 to 48 hours varied 35-fold (0.52–18.5 %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 to 48 hours. Patients were classified on the basis of 64Cu-MM-302 lesion deposition using a cut-off point that is comparable with 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 (HR = 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. Clin Cancer Res; 23(15); 4190–202. ©2017 AACR.

Fig. S1. MM-302 tumor deposition in preclinical xenografts as a function of HER2 expression. Fig. S2. Preclinical assessment of minimum 64Cu-MM-302 tumor delivery for anti-tumor activity. Fig. S3. Maximum intensity projection images of selected patients with average and rapid clearance of 64Cu-MM-302. Fig. S4. Stability of 64Cu-MM-302 in patients. Fig. S5. Hepatic tumor lesion with similar uptake as normal liver tissue. Fig. S6. Estimated parameter values (k1, k-1, VVF) for lesions from 3 patients who underwent 3 scans. Table S1. Model parameters for tracer kinetic modeling of 64Cu-MM-302 liposome transport into and out of tumors. Table S2. Patient Demographic (n=19). Table S3. Radiation Dosimetry of 64Cu-MM-302 (n=11).

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.

What you see is what you get—imaging techniques have long been essential for visualization and understanding of tissue development, homeostasis, and regeneration, which are driven by stem cell self-renewal and differentiation. Advances in molecular and tissue modeling techniques in the last decade are providing new imaging modalities to explore tissue heterogeneity and plasticity. Herein, we describe current state-of-the-art imaging modalities for tissue research at multiple scales, with a focus on explaining key tradeoffs such as spatial resolution, penetration depth, capture time/frequency, and moieties. We explore emerging tissue modeling and molecular tools which improve resolution, specificity, and throughput.

The complex and inaccessible space radiation environment poses an unresolved risk to astronaut cardiovascular health during long-term space exploration missions. To model this risk, healthy male c57BL/6 mice aged six months (corresponding to an astronaut of 34 years) were exposed to simplified galactic cosmic ray (GCR

As a cancer therapy, hypothermia has been used at sub-zero temperatures to cryosurgically ablate tumors. However, these temperatures can indiscriminately damage both tumorous and healthy cells. Additionally, strategies designed to kill tumor typically accelerate their evolution and recurrence can be inevitable in cancers such as glioblastoma (GBM). To bypass these limitations, here we studied the use of hypothermia as a cytostatic tool against cancer and deployed it against an aggressive rodent model of GBM. To identify the minimal dosage of ‘cytostatic hypothermia’, we cultured at least 4 GBM lines at 4 continuous or intermittent degrees of hypothermia and evaluated their growth rates through a custom imaging-based assay. This revealed cell-specific sensitivities to hypothermia. Subsequently, we examined the effects of cytostatic hypothermia on these cells by a cursory study of their cell-cycle, energy metabolism, and protein synthesis. Next, we investigated the use of cytostatic hypothermia as an adjuvant to chemotherapy and CAR T immunotherapy. Our studies demonstrated that cytostatic hypothermia did not interfere with Temozolomide in vitro and may have been synergistic against at least 1 GBM line. Interestingly, we also demonstrated that CAR T immunotherapy can function under cytostatic hypothermia. To assess the efficacy of hypothermia in vivo, we report the design of an implantable device to focally administer cytostatic hypothermia in an aggressive rodent model of F98 GBM. Cytostatic hypothermia significantly doubled the median survival of tumor-bearing rats with no obvious signs of distress. The absence of gross behavioral alterations is in concurrence with literature suggesting the brain is naturally resilient to focal hypothermia. Based on these findings, we anticipate that focally administered cytostatic hypothermia alone has the potential to delay tumor recurrence or increase progression-free survival in patients. Additionally, it could also provide more time to evaluate concomitant, curative cytotoxic treatments.

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.

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.

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.

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 is essential in building a bridge from basic science to the clinic by providing the confidence necessary to move new cancer therapies to patients. However, there is considerable variability in preclinical imaging, including tumor volume estimations based on tumor segmentation procedures which can be clearly user-biased. Our group is engaged in developing quantitative imaging methods which will be applied in the preclinical arm of a co-clinical trial studying synergy between anti-PD-1 treatment and radiotherapy using a genetically engineered mouse model of soft tissue sarcoma. This study focuses on a convolutional neural network (CNN)-based method for automatic tumor segmentation based on multimodal MRI images, i.e. T1 weighted, T2 weighted and T1 weighted with contrast agent. Our images were acquired on a 7.0 T Bruker Biospec small animal MRI scanner. Preliminary results show that our U-net structure and 3D patch-wise approach using both Dice and cross entropy loss functions delivers strong segmentation results. We have also compared single performance using only T2 weighted versus multimodal MR images for CNN segmentation. Our results showthat Dice similarity coefficient were higher when using multimodal versus single T2 weighted data (0.84 ± 0.05 and 0.81 ± 0.03). In conclusion, we successfully established a segmentation method for preclinical MR sarcoma data based on deep learning. This approach has the advantage of reducing user bias in tumor segmentation and improving the accuracy and precision of tumor volume estimations for co-clinical cancer trials.

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 R(2) 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: R(2) = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R(2) = 0.99, P < .001) and variance in nuclear circularity (best line fit: R(2) = 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

The study of animal models remains a cornerstone of preclinical cancer research. Recent advances in technology have led to the proliferation of animal imaging devices that are now integral to the study of cancer biology. Imaging is routinely used for the development and characterization of animal tumor models, measuring response to anticancer therapy, and for the detailed examination of individual molecular pathways within neoplastic tissue. The purpose of this chapter will be to provide an introduction to the most common imaging modalities used in contemporary oncologic investigation in order to help the reader determine if an imaging approach may complement their research goals.

Molecular imaging is a vital tool to non-invasively measure nanoparticle delivery to solid tumors. Despite the myriad of nanoparticles studied for cancer, successful applications of nanoparticles in humans is limited by inconsistent and ineffective delivery. Successful nanoparticle delivery in preclinical models is often attributed to enhanced permeability and retention (EPR)—a set of conditions that is heterogeneous and transient in patients. Thus, researchers are evaluating therapeutic strategies to modify nanoparticle delivery, particularly treatments which have demonstrated effects on EPR conditions. Imaging nanoparticle distribution provides a means to measure the effects of therapeutic intervention on nanoparticle delivery to solid tumors. This review focuses on the utility of imaging to measure treatment-induced changes in nanoparticle delivery to tumors and provides preclinical examples studying a broad range of therapeutic interventions.

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.