Raise standards for preclinical cancer research

Simple SummaryThe molecular progression of prostate cancer is complex and elusive. Biological research relies heavily on in vitro and in vivo models that can be used to examine gene functions and responses to the external agents in laboratory and preclinical settings. Over the years, several models have been developed and found to be very helpful in understanding the biology of prostate cancer. Here we describe these models in the context of available information on the cellular and molecular progression of prostate cancer to suggest their potential utility in basic and preclinical prostate cancer research. The information discussed herein should serve as a hands-on resource for scholars engaged in prostate cancer research or to those who are making a transition to explore the complex biology of prostate cancer.We have witnessed noteworthy progress in our understanding of prostate cancer over the past decades. This basic knowledge has been translated into efficient diagnostic and treatment approaches leading to the improvement in patient survival. However, the molecular pathogenesis of prostate cancer appears to be complex, and histological findings often do not provide an accurate assessment of disease aggressiveness and future course. Moreover, we also witness tremendous racial disparity in prostate cancer incidence and clinical outcomes necessitating a deeper understanding of molecular and mechanistic bases of prostate cancer. Biological research heavily relies on model systems that can be easily manipulated and tested under a controlled experimental environment. Over the years, several cancer cell lines have been developed representing diverse molecular subtypes of prostate cancer. In addition, several animal models have been developed to demonstrate the etiological molecular basis of the prostate cancer. In recent years, patient-derived xenograft and 3-D culture models have also been created and utilized in preclinical research. This review is an attempt to succinctly discuss existing information on the cellular and molecular progression of prostate cancer. We also discuss available model systems and their tested and potential utility in basic and preclinical prostate cancer research.

In recent years, with development of tumor animal model in pre clinical cancer research, based on small animal PET-CT predominance in the functional and anatomic imaging, researching tumor bearing animal tumor occurrence, development and treatment response has significant realistic meaning to guide clinical head and neck cancer prevention and control work. In this paper, the application progress of small animal PET-CT in head and neck cancer research in recent years is reviewed. Key words: Head and Neck Neoplasms; Small Animal PET-CT

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research. The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow. The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM). The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model. The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out. In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

The Reproducibility Project: Cancer Biology (RPCB) was established to provide evidence about reproducibility in basic and preclinical cancer research, and to identify the factors that influence reproducibility more generally. In this commentary we address some of the scientific, ethical and policy implications of the project. We liken the basic and preclinical cancer research enterprise to a vast 'diagnostic machine' that is used to determine which clinical hypotheses should be advanced for further development, including clinical trials. The results of the RPCB suggest that this diagnostic machine currently recommends advancing many findings that are not reproducible. While concerning, we believe that more work needs to be done to evaluate the performance of the diagnostic machine. Specifically, we believe three questions remain unanswered: how often does the diagnostic machine correctly recommend against advancing real effects to clinical testing?; what are the relative costs to society of false positive and false negatives?; and how well do scientists and others interpret the outputs of the machine?

The Reproducibility Project: Cancer Biology (RPCB) was established to provide evidence about reproducibility in basic and preclinical cancer research, and to identify the factors that influence reproducibility more generally. In this commentary we address some of the scientific, ethical and policy implications of the project. We liken the basic and preclinical cancer research enterprise to a vast 'diagnostic machine' that is used to determine which clinical hypotheses should be advanced for further development, including clinical trials. The results of the RPCB suggest that this diagnostic machine currently recommends advancing many findings that are not reproducible. While concerning, we believe that more work needs to be done to evaluate the performance of the diagnostic machine. Specifically, we believe three questions remain unanswered: how often does the diagnostic machine correctly recommend against advancing real effects to clinical testing?; what are the relative costs to society of false positive and false negatives?; and how well do scientists and others interpret the outputs of the machine?

Cancer tissue smears are routinely used in rapid intraoperative pathology workflows using quick staining methods to characterize cancer in surgical margin assessments or tumor pathology. Mass spectrometry (MS) is a sensitive analytic platform that can detect the presence of cancer from the pattern of cancer-specific molecules present in the mass spectrum of the tissue under examination. In particular, mass spectrometry analysis with desorption electrospray ionization (DESI-MS) is shown to have utility in research models for cancer characterization or even for grading different subclasses of disease based on tumor-specific small molecule lipid or metabolites. DESI does not require extensive tissue preparation, and the data collection and analysis can be done within a few seconds. In this work, we evaluate the combination of rapid DESI-MS detection with rapid tissue smear preparation for research use in preclinical xenograft models of breast cancer and pediatric medulloblastoma requiring only seconds of sampling, and an overall preparation and analysis time of less than one minute. Principal component analysis (PCA) was performed to evaluate the concordance between DESI-MS profiles of breast cancer from tissue slices and smears prepared on various surfaces. PCA suggested no statistical discrimination between DESI-MS profiles of tissue sections and tissue smears prepared on glass, polytetrafluoroethylene (PTFE), and porous PTFE. However, the abundances of cancer biomarker ions varied between sections and smears, with DESI-MS analysis of tissue sections yielding higher ion abundances of cancer biomarkers compared with smears. The coefficient of variance (CV) analysis suggests DESI-MS profiles from tissue smears are as reproducible as the ones from tissue sections. The limit of detection with smear samples from single pixel analysis is comparable to tissue sections that average the signal from a tissue area of 0.01 mm2. The smears prepared on the PTFE surface possessed a higher degree of homogeneity compared with the smears prepared on the glass surface. This allowed single MS scans (~1 s) from random positions across the surface of the smear to be used in rapid cancer typing with good reproducibility, providing useful pathologic information at speeds suitable for research use. Likewise, DESI-MS enabled the rapid classification of subgroups of medulloblastoma in these preclinical models. Citation Format: Michael Woolman, Alessandra Tata, Isabelle Ferry, Claudia Kuzan-Fischer, Megan Wu, Sunit Das, Michael D. Taylor, James T. Rutka, Howard J. Ginsberg, Arash Zarrine-Afsar. Rapid, non-subjective characterization of disease in preclinical cancer research using desorption electrospray ionization mass spectrometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4118.

In preclinical imaging studies with small animals, tracking tumor growth accurately over time can be challenging due to the time-consuming, labor-intensive, and error-prone nature of manual segmentation of 3D images. Orthotopic and metastatic cancer models can be particularly problematic as additional radiologic expertise is required to detect tumors within the context of deep anatomy, and individual animals may contain many nodules (ten or more) requiring many hours of segmentation effort. Therefore, the goal of this study was to develop and validate a deep learning model for automatic volumetric measurement of murine liver tumors in MR images to increase the practicality and adoptability of longitudinal imaging in preclinical research. We describe a method for automatically quantifying tumor volume over time using a dataset of 4.7T T2-weighted MR images taken from a study involving a genetic model of spontaneous liver cancer. The dataset included 894 images from 198 unique animals collected using a Biospec 47/40 (Bruker, Kontich, Belgium) over a span of 30 weeks. Each image was manually segmented by an expert annotator and tumor burden was tracked longitudinally to generate growth curves. A modified U-Net model built on the MONAI framework was trained to segment tumors. On a held-out test subset of 177 images (20% of the overall dataset), the model's predicted overall tumor burden showed a strong correlation with the human ground truth, achieving an R2 value of 0.88. The model processed each image in 0.34 seconds on a machine with an NVIDIA RTX 3090 GPU, significantly faster than the 5-10 minutes required for manual annotation. This efficiency translates to a substantial reduction in overall annotation time for studies with large datasets. This study demonstrates the feasibility of using deep learning for liver tumor segmentation in MR images. The proposed method offers a reliable and efficient alternative to manual segmentation, potentially improving the accuracy and speed of volumetric measurements in preclinical cancer research. Citation Format: Thomas M. Kierski, Adam M. Aji, Juan D. Rojas, Ryan C. Gessner, Laura Beretta, Suet Ying Kwan, John Hazle, Kiersten L. Maldonado, Charles Kingsley, Richard R. Bouchard, Tomasz J. Czernuszewicz. Automated quantification of small animal liver tumor volume in longitudinal MR imaging using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7444.

Immunotherapy for prostate cancer has recently emerged as an attractive treatment strategy. Yet, preclinical models where relationship between inflammation, stroma, tumor cells and prostate cancer progression can be studied are limited. GEMM models of prostate cancer are scarce and in xenograft models, even when in humanized mice, the role of immune system in the initiation and in progress of the malignancy cannot be studied. As the requirement to test novel immunotherapies and especially combination treatments is increasing, a preclinical model that takes into account tumor microenvironment and immune system would be highly useful to promote development of novel therapies to combat against prostate cancer. Aim of the present study was to reveal if there is role between the immune system and development of prostate cancer, and secondly, to validate a model to be utilized later in immunotherapy development. Intact 10-12 weeks old male Noble rats were s.c. implanted with slow-releasing estradiol and testosterone pellets for 6, 13 and 18 weeks. Daily release for testosterone was 0.8 mg and for estradiol 0.08 mg. Control group animals received placebo hormone pellets without hormones. Serum samples were collected during the study to monitor hormone levels, and prostates were removed and processed for histopathological evaluation at the end of the study. Hormonal treatment caused an increase in estradiol to testosterone ratio, and the prostates were enlarged. Imbalance in hormone-milieu induced inflammation in the prostate, followed by formation of prostatic intraepithelial neoplasia (PIN)-like lesions and finally adenocarcinomas in the periurethral region. Inflammatory cells, mainly T-cells were noticed in the vicinity of PIN-like lesions. During the progression of prostate cancer, inflammatory cells disappeared from the adenocarcinoma sites. In the prostate, inflammation consisting of perivascular, stromal and periglandular T-lymphocytes and intraluminal neutrophils remained. Results of this study indicate significance of hormonal milieu, especially estrogens and androgens, in the development of inflammation and progression of prostate cancer, with a key role for tumor microenvironment. Presence of lymphocytes in the proximity of PIN-like lesions during the early phases of prostate cancer, and their disappearance later in the adenocarcinomas, indicate interaction between innate and adaptive immune system and cancer. Therefore, this preclinical prostate cancer model that combines immune system and cancer can be utilized when new immunotherapies, combination treatments and prevention possibilities against prostate cancer progression are developed. Citation Format: Mari I. Suominen, Tiina Kähkönen, Yvonne Konkol, Jenni Mäki-Jouppila, Jussi M. Halleen, Jenni Bernoulli. Preclinical efficacy model to promote immunotherapy development for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1663. doi:10.1158/1538-7445.AM2017-1663

Breast cancer expressing estrogen receptor (ER) and progesterone receptor (PR) is classified as hormone receptor positive, and triple-positive breast cancer expresses also human epidermal growth factor receptors (HER2). Despite the hormonal and HER2 targeted treatments, breast cancer metastasizes to bone in high frequency and may develop resistance against the used treatment. Prevention and treatment of bone metastases is challenging and moreover, hormones are strong regulators of both bone and immune system. Aim of the present study was to verify and compare ER, PR, HER2 and also HER3 status in preclinical primary and bone metastasis breast cancer models utilizing immunodeficient and human immune system engrafted mice. BT-474 human breast cancer cells were inoculated orthotopically into mammary fat pad of placebo or 17β-estradiol (E2) supplemented female immunodeficient NOG mice. In a bone tumor study, BT-474 cells were inoculated into the tibia of female NOG or humanized NOG mice (HSCFTL-NOG-F mice, Taconic Biosciences). Tumor growth was followed for 8 weeks and histopathological tumor evaluation and immunohistochemical stainings for ER, PR, HER2 and HER3 were performed. Orthotopic tumor growth of BT-474 was hormone dependent and only minor growth was observed in the absence of E2. In the presence of E2 supplement, the orthotopic tumor expressed ER, PR and HER2/HER3. However, in the absence of E2 supplement there was reduced PR expression but no major changes in the ER and HER2/HER3 expression. In contrast, when breast cancer cells were inoculated into the tibia, tumor growth was observed also without E2 supplement. In this case, tumor in the bone was positive for ER and HER2/HER3 but negative for PR. No significant changes were observed between immunodeficient and humanized mice regarding intratibial tumor growth or ER, PR and HER2/HER3 expression. As a summary, estrogen supplementation is needed to support breast cancer BT-474 tumor growth when cancer cells are inoculated orthotopically into mammary fat pad. In contrast, BT-474 tumor growth was observed in bone also in the absence of supplied E2 in immunodeficient and humanized mice. ER and HER2/HER3 expression was observed in primary and bone tumors, but PR expression was significantly reduced if no estrogen supplement was used. Taking together, when developing new therapies against breast cancer, treatment targets in preclinical models should be carefully verified. Focus should be addressed not only on primary tumor but also on bone metastasis where cancer cells are under influence of different tumor microenvironment and may express differently hormone receptors and HER2/HER3. While hormones influence breast cancer progression, they also regulate bone turnover and immune system, and therefore humanized mouse models provide an essential platform for novel therapy development. Citation Format: Tiina E. Kähkönen, Mari I. Suominen, Jussi M. Halleen, Jenni H. Mäki-Jouppila, Azusa Tanaka, Michael Seiler, Teppo Haapaniemi, Jenni Bernoulli. Hormone receptor and HER2/HER3 expression in preclinical breast cancer models of primary tumor and bone metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3859.

Development of a Novel Preclinical Pancreatic Cancer Research Model: Bioluminescence Image- Guided Focal Irradiation and Tumor Monitoring of Orthotopic Xenografts

Introduction: Breast cancer expressing estrogen receptor (ER) and progesterone receptor (PR) is classified as hormone receptor positive, and triple-positive breast cancer expresses also human epidermal growth factor receptor (HER2). Despite the hormonal and HER2 targeted treatments, breast cancer metastasizes to bone in high frequency and may develop resistance against the used treatment. Prevention and treatment of bone metastases is challenging; moreover, hormones are strong regulators of both bone and immune system. The aim of the present study was to verify ER, PR, HER2, and also HER3 status in preclinical primary and bone metastasis breast cancer models utilizing immunodeficient and human immune system engrafted mice in order to verify predictive models for drug development. Materials and Methods: BT-474 human breast cancer cells were inoculated orthotopically into mammary fat pad of placebo or 17β-estradiol (E2) supplemented female immunodeficient NOG mice. In a bone tumor study, BT-474 cells were inoculated into the tibia of female NOG or humanized NOG mice (HSCFTL-NOG-F mice, Taconic Biosciences). Tumor growth was followed for 8 weeks, and histopathologic tumor evaluation and immunohistochemical stainings for ER, PR, HER2, and HER3 were performed. Results: Orthotopic tumor growth of BT-474 was hormone dependent and only minor growth was observed in the absence of estrogen. In the presence of E2 supplement, the orthotopic tumor expressed ER, PR, and HER2/HER3. However, in the absence of E2 supplement there was reduced PR expression but no major changes in the ER and HER2/HER3 expression. In contrast, when breast cancer cells were inoculated into the tibia, tumor growth was observed also without E2 supplement. In this case, tumor in the bone was positive for ER and HER2/HER3 but negative for PR. No significant changes were observed between immunodeficient and humanized mice regarding intratibial tumor growth and ER, PR, and HER2/HER3 expression. Conclusions: Estrogen supplementation is needed to support breast cancer BT-474 tumor growth when cancer cells are inoculated orthotopically into mammary fat pad. In contrast, BT-474 tumor growth was observed in bone even in the absence of supplied estrogen. ER and HER2/HER3 expression was observed in primary and bone tumors, but PR expression was significantly reduced if no estrogen supplement was used. Differences in tumor growth depending on the site and estrogen level highlight the importance of tumor microenvironment in breast cancer, and also refer why tumor may shift resistant to used hormonal or HER2 targeted therapy. Taking together, when developing new therapies against breast cancer, treatment targets in preclinical models should be carefully verified. Focus should be placed not only on primary tumor but also on bone metastasis where cancer cells are under influence of different tumor microenvironment and may express differently hormone receptors and HER2/HER3. While hormones influence breast cancer progression, they also regulate bone turnover and immune system, and therefore humanized mouse models provide an essential platform for novel therapy development. Citation Format: Tiina Kähkönen, Mari I. Suominen, Jenni Mäki-Jouppila, Jussi M. Halleen, Azusa Tanaka, Michael Seiler, Teppo Haapaniemi, Jenni Bernoulli. Importance of hormone receptor and HER2/HER3 status verification in preclinical breast cancer models using immunodeficient and humanized mice [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B099.

Cisplatin, a primary chemotherapeutic drug, is of great value in the realm of tumor treatment. However, its clinical efficacy is strictly hindered by issues, such as drug resistance, relapse, poor prognosis, and toxicity to normal tissue. Cisplatin-based combination therapy has garnered increasing attention in both preclinical and clinical cancer research for its ability to overcome resistance, reduce toxicity, and enhance anticancer effects. This review examines three primary co-administration strategies of cisplatin-based drug combinations and their respective advantages and disadvantages. Additionally, seven types of combination therapies involving cisplatin are discussed, focusing on their main therapeutic effects, mechanisms in preclinical research, and clinical applications. This review also discusses future prospects and challenges, aiming to offer guidance for the development of optimal cisplatin-based combination therapy regimens for improved cancer treatment.

Recently, Begley and Ellis delivered a sobering message to laboratories around the world (1). The lack of meaningful progress in preclinical cancer research was highlighted by the irreproducibility of >70% of published studies. The authors also crystallized the importance of full disclosure and the validation of critical scientific discoveries for industry-wide improvement. Translation of novel biomarkers into clinical care for the evaluation of therapeutic safety and efficacy has been slow (2), partly because of the cost and complexity of immunoassay development. The potential for liquid chromatography–tandem mass spectrometry (LC-MS/MS)3 to streamline the translation of novel protein biomarkers is profound (3). Most LC-MS/MS-based protein assays incorporate denaturation and proteolytic digestion of proteins in the sample into peptides (traditionally called “bottom-up” proteomics). These preparative steps destroy potentially interfering proteins into peptides that can be resolved and ignored by LC-MS/MS (4). Inclusion of stable isotope–labeled internal standard proteins or peptides (which may be cleavable) in each sample enables correction for matrix effects, including sample-related digestion variability and/or ion suppression, both significant analytical benefits compared with immunoassays. Downstream members of the scientific community are hopeful about translating important preliminary findings into clinical practice; however, success has been hampered by a lack of transparency and insufficient validation. Consequently, LC-MS/MS-based clinical protein analysis has predominantly focused on improved analytical measurement for well-established biomarkers (5). This is despite “fit-for-purpose” criteria for enablement (6, 7) and published recommendations for analytical validation (8), based primarily on U.S. Food and Drug Administration guidance (9). Although assays used in preclinical research are generally not held to the same standards as assays used in the immediate care of patients, which are governed by CLIA-88 and by extension many Clinical Laboratory Standards Institute consensus documents, published fundamental discovery experiments and biomarker verification studies spawn costly research programs. To advance our …

The formation of new blood vessels from previous ones, angiogenesis, is critical in tissue repair, expansion or remodeling in physiological processes and in various pathologies including cancer. Despite that, the development of anti-angiogenic drugs has great potential as the treatment of cancer faces many problems such as development of the resistance to treatment or an improperly selected therapy approach. An evaluation of predictive markers in personalized medicine could significantly improve treatment outcomes in many patients. This comprehensive review emphasizes the anticancer potential of flavonoids mediated by their anti-angiogenic efficacy evaluated in current preclinical and clinical cancer research. Flavonoids are important groups of phytochemicals present in common diet. Flavonoids show significant anticancer effects. The anti-angiogenic effects of flavonoids are currently a widely discussed topic of preclinical cancer research. Flavonoids are able to regulate the process of tumor angiogenesis through modulation of signaling molecules such as VEGF, MMPs, ILs, HIF or others. However, the evaluation of the anti-angiogenic potential of flavonoids within the clinical studies is not frequently discussed and is still of significant scientific interest.

192 Background: Preclinical pancreatic cancer animal models for radiation research are far from optimal because they utilize nonlocalized, single-beam irradiation of large fields due to lack of accurate targeting and delivery. We report on a novel preclinical pancreatic cancer research model that utilizes bioluminescence imaging (BLI)-guided irradiation (RT) of orthotopic xenograft tumors, sparing of surrounding normal tissues and quantitative, noninvasive longitudinal assessment of treatment response. Methods: In accordance with institutional guidelines, luciferase-expressing MiaPaCa-2 pancreatic carcinoma cells were used to generate orthotopic pancreatic tumors in nude mice. BLI of tumors were correlated to PET/CT and necropsy specimens using Pearson correlation. BLI was compared to cone-beam CT (CBCT) to determine the location of the tumor centroid and estimate an appropriate margin for radiation planning. Off-line fusion of BLI with CBCT was performed to guide radiation delivery to tumors using our small animal radiation research platform (SARRP). RT-induced DNA damage was assessed by γ-H2Ax and p-ATM foci. BLI was used to longitudinally monitor radiation treatment response and was correlated to necropsy specimen. Results: BLI accurately predicted tumor volume (R2 = 0.9961) and correlated well with PET/CT imaging of tumors (R2 = 0.97). BLI centroid accuracy was 3.5 mm relative to that of the CBCT. Irradiated pancreatic tumors stained positively for γ-H2Ax and p-ATM, while surrounding organs were spared. Longitudinal assessment of irradiated (5 Gy) tumors with BLI revealed a significant tumor growth delay of 20 days relative to untreated controls. This was also confirmed pathologically as mean tumor volume of irradiated mice was 30.2% that of unirradiated mice (p < 0.05). Conclusions: We have developed a bioluminescent, orthotopic preclinical pancreas cancer model that allows noninvasive 1) normalizing of pretreatment tumor burden; 2) treatment planning and image-guided focal RT therapy; and 3) longitudinal assessment of treatment response. This unique translational model offers a means to investigate targeted and systemic agents with focused RT for pancreatic cancer. No significant financial relationships to disclose.