A quantitative volumetric micro-computed tomography method to analyze lung tumors in genetically engineered mouse models

Comparison of trastuzumab emtansine, trastuzumab deruxtecan, and disitamab vedotin in a multiresistant HER2-positive breast cancer lung metastasis model

Human epidermal growth factor 2 (HER2)-positive breast cancer with lung metastases resistant to targeted agents is a common therapeutic challenge. Absence of preclinical lung metastasis models that are resistant to multiple anti-HER2 targeted drugs hampers the development of novel therapies. We established a novel HER2-positive breast cancer cell line (L-JIMT-1) with a high propensity to form lung metastases from the parenteral JIMT-1 cell line by injecting JIMT-1 cells into immunodeficient SCID mice. Lung metastases developed in all mice injected with L-JIMT-1 cells, and more rapidly and in greater numbers compared with the parental JIMT-1 cells. L-JIMT-1 cells expressed more epidermal growth factor receptor and HER2 than JIMT-1 cells. L-JIMT-1 cells were resistant to all five tyrosine kinase inhibitors tested in vitro (afatinib, erlotinib, lapatinib, sapitinib, and tucatinib). When we compared JIMT-1 and L-JIMT-1 sensitivity to three HER2-targeting antibody-drug conjugates (ADCs) trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), and disitamab vedotin (DV) in vitro, JIMT-1 cells were resistant T-DXd, partially sensitive to T-DM1, and sensitive to DV, while L-JIMT-1 cells were resistant to both T-DM1 and T-DXd, but moderately sensitive to DV. In a mouse model, all three ADCs inhibited the growth of L-JIMT-1 lung metastases compared to a vehicle, but DV and T-DXd more strongly than T-DM1, and DV treatment led to the smallest tumor burden. The L-JIMT breast cancer lung metastasis model developed may be useful in the evaluation of anti-cancer agents for multiresistant HER2-positive advanced breast cancer.

Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography

Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r2=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r2=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r2=0.98, DSC=0.78) performed within inter-reader variability (r2=0.79, DSC=0.69) and close to intra-reader variability (r2=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.

An optimized protocol for the generation and monitoring of conditional orthotopic lung cancer in the KP mouse model using an adeno-associated virus vector compatible with biosafety level 1

BACKGROUND

The inducible Kras/p53 lung adenocarcinoma mouse model, which faithfully recapitulates human disease, is routinely initiated by the intratracheal instillation of a virus-based Cre recombinase delivery system. Handling virus-based delivery systems requires elevated biosafety levels, e.g., biosafety level 2 (BSL-2). However, in experimental animal research facilities, following exposure to viral vectors in a BSL-2 environment, rodents may not be reclassified to BSL-1 according to standard practice, preventing access to small animal micro-computed tomography (micro-CT) scanners that are typically housed in general access areas such as BSL-1 rooms. Therefore, our goal was to adapt the protocol so that the Cre-induced KP mouse model could be handled under BSL-1 conditions during the entire procedure.

RESULTS

The Kras-Lox-STOP-Lox-G12D/p53 flox/flox (KP)-based lung adenocarcinoma mouse model was activated by intratracheal instillation of either an adenoviral-based or a gutless, adeno-associated viral-based Cre delivery system. Tumor growth was monitored over time by micro-CT. We have successfully substituted the virus-based Cre delivery system with a commercially available, gutless, adeno-associated, Cre-expressing vector that allows the KP mouse model to be handled and imaged in a BSL-1 facility. By optimizing the anesthesia protocol and switching to a microscope-guided vector instillation procedure, productivity was increased and procedure-related complications were significantly reduced. In addition, repeated micro-CT analysis of individual animals allowed us to monitor tumor growth longitudinally, dramatically reducing the number of animals required per experiment. Finally, we documented the evolution of tumor volume for different doses, which revealed that individual tumor nodules induced by low-titer AAV-Cre transductions can be monitored over time by micro-CT.

CONCLUSION

Modifications to the anesthesia and instillation protocols increased the productivity of the original KP protocol. In addition, the switch to a gutless, adeno-associated, Cre-expressing vector allowed longitudinal monitoring of tumor growth under BSL-1 conditions, significantly reducing the number of animals required for an experiment, in line with the 3R principles.

Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice

X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4-8 min, and data analysis typically requires 10-30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents.

Streptococcus pneumoniae promotes lung cancer development and progression

Streptococcus pneumoniae (SP) is associated with lung cancer, yet its role in the tumorigenesis remains uncertain. Herein we find that SP attaches to lung cancer cells via binding pneumococcal surface protein C (PspC) to platelet-activating factor receptor (PAFR). Interaction between PspC and PAFR stimulates cell proliferation and activates PI3K/AKT and nuclear factor kB (NF-kB) signaling pathways, which trigger a pro-inflammatory response. Lung cancer cells infected with SP form larger tumors in BALB/C mice compared to untreated cells. Mice treated with tobacco carcinogen and SP develop more lung tumors and had shorter survival period than mice treated with the carcinogen alone. Mutating PspC or PAFR abolishes tumor-promoting effects of SP. Overabundance of SP is associated with the survival. SP may play a driving role in lung tumorigenesis by activating PI3K/AKT and NF-kB pathways via binding PspC to PAFR and provide a microbial target for diagnosis and treatment of the disease.

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.

In Vivo Modeling of Tumor Heterogeneity for Immuno-Oncology Studies: Failures, Improvements, and Hopes

Murine tumor modeling is fundamental for the preclinical development of anti-cancer therapies. Use of immunocompetent mouse models is becoming increasingly relevant as we gain more knowledge of how cancer cells interact with the immune system in the tumor microenvironment and how we can harness the immune system to fight tumors. However, there are few intrinsically immunogenic preclinical tumor models, and the vast majority either do not respond to therapy or do not faithfully predict the responses of the therapy when applied in the clinic. Here, we discuss the limitations of commonly used murine tumor models in immuno-oncology and strategies to improve their immunogenicity and mutational burden to more accurately reflect the heterogeneity of patient tumors. © 2022 Wiley Periodicals LLC.

Usefulness of microfocus computed tomography in life science research: preliminary study using murine micro-hepatic tumor models

PURPOSE

Microfocus computed tomography (micro-CT) has not been widely used at high radiation intensity (industrial micro-CT) in life science fields. In this preliminary study, we investigated its potential value in the detection of micro-hepatic tumors in a mouse model.

METHODS

The liver with micro-hepatic tumors was surgically resected en-bloc from mice, and examined with industrial micro-CT and lower intensity micro-CT (small animal micro-CT). The number of hepatic tumors was manually counted on serial images. Then, the accuracy of each technique was determined by preparing matching liver sections and comparing the number of tumors identified in a conventional pathological examination.

RESULTS

The number of hepatic tumors evaluated with industrial micro-CT showed high concordance with the results of the pathological examinations (intraclass correlation coefficient [ICC]: 0.984; 95% confidence interval [CI] 0.959-0.994). On the other hand, the number of hepatic tumors evaluated with the small animal micro-CT showed low concordance with the number identified in the pathological examinations (ICC: 0.533; 95% CI 0.181-0.815).

CONCLUSION

Industrial micro-CT improved the detection of small structures in resected specimens, and might be a promising solution for life science research.

Accurately Locating Metastatic Foci in Lymph Nodes With Lugol's Iodine-Enhanced Micro-CT Imaging

Background

Accurate evaluation of lymph node (LN) status is the key factor to determine the treatment and evaluate prognosis for patients with cancer. However, traditional pathological examination resulted in a 30% false-negative rate of detection of metastases in LNs. This study aimed to utilize Lugol's iodine (I₂-IK)-enhanced micro-CT imaging to reveal the 3-dimensional structure of regional LNs and decrease the false-negative rate in pathological examination.

Methods

To explore the feasibility of I₂-IK-enhanced micro-CT imaging in locating metastatic lesion in LNs, nonmetastatic and metastatic LNs from mice were used to mimic the imaging process. Then, the LNs from oral squamous cell carcinoma (OSCC) patients were applied to verify the value of I₂-IK-enhanced micro-CT imaging in revealing LN structure and locating metastatic lesions in LNs. The glycogen content in nonmetastatic and metastatic LNs was further detected by the use of a glycogen assay kit and periodic acid-Schiff (PAS) staining to explain the imaging differences between them.

Results

In nude mice, 0.5% I₂-IK staining for 4 h was the best parameter for normal LN. The metastatic foci in metastatic LNs were also clearly outlined in this condition. For nonmetastatic LNs from patients with OSCC, 1% I₂-IK staining for 12 h was the best parameter. However, due to the increased volume of metastatic LNs, the image effect of 3% I₂-IK staining for 12 h was superior to 1% I₂-IK staining [tumor background ratio (TBR), 3% vs. 1%, 1.89 ± 0.10 vs. 1.27 ± 0.07, p < 0.001]. Compared with subsequent pathological sections, we found the CT intensity of metastatic foci in LNs and muscle tissues was significantly higher than in nonmetastatic regions. Meanwhile, the glycogen content of metastatic foci in LNs detected was also significantly higher than in nonmetastatic region.

Conclusions

I₂-IK-enhanced micro-CT imaging could identify the spatial location of metastatic foci in LNs. This will be an effective method to assist in decreasing the LN false-negative rate for cancer pathology.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.

Human-specific GAPDH qRT-PCR is an accurate and sensitive method of xenograft metastasis quantification

Metastasis is the primary cause of cancer-related mortality. Experimental models that accurately reflect changes in metastatic burden are essential tools for developing treatments and to gain a better understanding of disease. Murine xenograft tumor models mimic the human scenario and provide a platform for metastasis analyses. An ex vivo quantitative method, gaining favor for its ease and accuracy, is quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR); however, it is currently unclear how well this method correlates with gold-standard histological analysis, and its use has required detection of overexpressed exogenous genes. We have introduced a variation of the qRT-PCR method: human-specific glyceraldehyde 3-phosphate dehydrogenase (GAPDH) qRT-PCR, which allows quantification of metastasis in xenograft models without the requirement of overexpressed exogenous genes. This makes the method easily amenable to many xenograft models without alteration of the cancer cells. We determined that the method is able to detect a few human cells within abundant mouse lung tissue. Further, the human-specific GAPDH qRT-PCR is more sensitive and correlates with histological analysis in terms of determining relative metastatic burden, suggesting that human-specific GAPDH qRT-PCR could be used as a primary method for quantification of disseminated human cells in murine xenograft models.

Monitoring treatment effects in lung cancer-bearing mice: clinical CT and clinical MRI compared to micro-CT

Background

Compared to histology-based methods, imaging can reduce animal usage in preclinical studies. However, availability of dedicated scanners is limited. We evaluated clinical computed tomography (CT) and magnetic resonance imaging (MRI) in comparison to dedicated CT (micro-CT) for assessing therapy effects in lung cancer-bearing mice.

Methods

Animals received cisplatin (n = 10), sham (n = 12), or no treatment (n = 9). All were examined via micro-CT, CT, and MRI before and after treatment. Semiautomated tumour burden (TB) calculation was performed. The Bland-Altman, receiver operating characteristic (ROC), and Spearman statistics were used.

Results

All modalities always allowed localising and measuring TB. At all modalities, mice treated with cisplatin showed a TB reduction (p ≤ 0.012) while sham-treated and untreated individuals presented tumour growth (p < 0.001). Mean relative difference (limits of agreement) between TB on micro-CT and clinical scanners was 24.7% (21.7–27.7%) for CT and 2.9% (−4.0–9.8%) for MRI. Relative TB changes before/after treatment were not different between micro-CT and CT (p = 0.074) or MRI (p = 0.241). Mice with cisplatin treatment were discriminated from those with sham or no treatment at all modalities (p ≤ 0.001). Using micro-CT as reference standard, ROC areas under the curves were 0.988–1.000 for CT and 0.946–0.957 for MRI. TB changes were highly correlated across modalities (r ≥ 0.900, p < 0.001).

Conclusions

Clinical CT and MRI are suitable for treatment response evaluation in lung cancer-bearing mice. When dedicated scanners are unavailable, they should be preferred to improve animal welfare.

A step towards valid detection and quantification of lung cancer volume in experimental mice with contrast agent-based X-ray microtomography

Tumor volume is a parameter used to evaluate the performance of new therapies in lung cancer research. Conventional methods that are used to estimate tumor size in mouse models fail to provide fast and reliable volumetric data for tumors grown non-subcutaneously. Here, we evaluated the use of iodine-staining combined with micro-computed tomography (micro-CT) to estimate the tumor volume of ex vivo tumor-burdened lungs. We obtained fast high spatial resolution three-dimensional information of the lungs, and we demonstrated that iodine-staining highlights tumors and unhealthy tissue. We processed iodine-stained lungs for histopathological analysis with routine hematoxylin and eosin (H&E) staining. We compared the traditional tumor burden estimation performed manually with H&E histological slices with a semi-automated method using micro-CT datasets. In mouse models that develop lung tumors with well precise boundaries, the method that we describe here enables to perform a quick estimation of tumorous tissue volume in micro-CT images. Our method overestimates the tumor burden in tumors surrounded by abnormal tissue, while traditional histopathological analysis underestimates tumor volume. We propose to embed micro-CT imaging to the traditional workflow of tumorous lung analyses in preclinical cancer research as a strategy to obtain a more accurate estimation of the total lung tumor burden.

A Novel Method for Quantifying Total Thoracic Tumor Burden in Mice

Mouse models are powerful tools to study lung cancer initiation and progression in vivo and have contributed significantly to recent advances in therapy. Using micro-computed tomography to monitor and study parenchymal and extra-parenchymal metastases in existing murine models of lung cancer is challenging owing to a lack of radiographic contrast and difficulty in achieving respiratory gating. To facilitate the analysis of these in vivo imaging studies and study of tumor progression in murine models we developed a novel, rapid, semi-automated method of calculating thoracic tumor burden from computed tomography images. This method, in which commercially available software is used to calculate the mass of the thoracic cavity (MTC), takes into account the aggregate tumor burden in the thoracic cavity. The present study showed that in tumor-free mice, the MTC does not change over time and is not affected by breathing, whereas in tumor-bearing mice, the increase in the MTC is a measure of tumor mass that correlates well with tumor burden measured by lung weight. Tumor burden calculated with our MTC method correlated with that measured by lung weight as well as or better than that calculated using four established methods. To test this method, we assessed metastatic tumor development and response to a pharmacologic PLK1 inhibitor in an orthotopic xenograft mouse model. PLK1 inhibition significantly inhibited tumor growth. Our results demonstrate that the MTC method can be used to study dynamic changes in tumor growth and response to therapeutics in genetically engineered mouse models and orthotopic xenograft mouse models of lung cancer.

Volumetric and linear measurements of lung tumor burden from non-gated micro-CT imaging correlate with histological analysis in a genetically engineered mouse model of non-small cell lung cancer

In vivo micro-computed tomography (CT) imaging allows longitudinal studies of pulmonary neoplasms in genetically engineered mouse models. Respiratory gating increases the accuracy of lung tumor measurements but lengthens anesthesia time in animals that may be at increased risk for complications. We hypothesized that semiautomated, volumetric, and linear tumor measurements performed in micro-CT images from non-gated scans would have correlation with histological findings. Primary lung tumors were induced in eight FVB mice with two transgenes (FVB/N-Tg(tetO-Kras2)12Hev/J; FVB.Cg-Tg(Scgb1a1-rtTA)1Jaw/J). Non-gated micro-CT scans were performed and the lungs were subsequently harvested. In the acquired micro-CT scans, measurements of all identified tumors were determined using the following methods: semiautomated three-dimensional (3D) volume, ellipsoid volume, Response Evaluation Criteria in Solid Tumors (RECIST; sum of largest axial (i.e., transverse) diameter from five tumors), sum of largest axial diameters from all tumors (modified RECIST), and average axial diameter. For histological analysis, all five lung lobes were analyzed and the tumor area was summed from measurements made on five histological sections that were 300 µm apart from each other (covering a total depth of 1200 µm). All micro-CT measurement methods had very strong correlation with histological tumor burden (Pearson's correlation coefficient, 0.87 ( p = 0.0053) -0.98 ( p < 0.0001)). The only methods found to have different correlations were the semiautomated 3D method and the RECIST method (Williams' test for dependent overlapping correlations, p = 0.013). Our results suggest quantification of lung tumor burden from non-gated micro-CT imaging will reflect histological differences between mice and can therefore be used for between-group comparisons or when concerns about systemic health of research animals may limit lengthy anesthetic procedures.

In Vivo Imaging in Pharmaceutical Development and Its Impact on the 3Rs

It is well understood that the biopharmaceutical industry must improve efficiency along the path from laboratory concept to commercial product. In vivo imaging is recognized as a useful method to provide biomarkers for target engagement, treatment response, safety, and mechanism of action. Imaging biomarkers have the potential to inform the selection of drugs that are more likely to be safe and effective. Most of the imaging modalities for biopharmaceutical research are translatable to the clinic. In vivo imaging does not require removal of tissue to provide biomarkers, thus reducing the number of valuable preclinical subjects required for a study. Longitudinal imaging allows for quantitative intra-subject comparisons, enhancing statistical power, and further reducing the number of subjects needed for the evaluation of treatment effects in animal models. The noninvasive nature of in vivo imaging also provides a valuable approach to alleviate or minimize potential pain, suffering or distress.

Early clinical applications for imaging at microscopic detail: microfocus computed tomography (micro-CT)

Microfocus CT (micro-CT) has traditionally been used in industry and preclinical studies, although it may find new applicability in the routine clinical setting. It can provide high-resolution three-dimensional digital imaging data sets to the same level of detail as microscopic examination without the need for tissue dissection. Micro-CT is already enabling non-invasive detailed internal assessment of various tissue specimens, particularly in breast imaging and early gestational fetal autopsy, not previously possible from more conventional modalities such as MRI or CT. In this review, we discuss the technical aspects behind micro-CT image acquisition, how early work with small animal studies have informed our knowledge of human disease and the imaging performed so far on human tissue specimens. We conclude with potential future clinical applications of this novel and emerging technique.

Noninvasive Bioluminescence Imaging of AKT Kinase Activity in Subcutaneous and Orthotopic NSCLC Xenografts: Correlation of AKT Activity with Tumor Growth Kinetics

Aberrant signaling through the AKT kinase mediates oncogenic phenotypes including cell proliferation, survival, and therapeutic resistance. Here, we utilize a bioluminescence reporter for AKT kinase activity (BAR) to noninvasively assess the therapeutic efficacy of the EGFR inhibitor erlotinib in KRAS-mutated lung cancer therapy. A549 non–small cell lung cancer cell line, engineered to express BAR, enabled the evaluation of compounds targeting the EGFR/PI3K/AKT pathway in vitro as well as in mouse models. We found that erlotinib treatment of resistant A549 subcutaneous and orthotopic xenografts resulted in significant AKT inhibition as determined by an 8- to 13-fold (P < .0001) increase in reporter activity 3 hours after erlotinib (100 mg/kg) administration compared to the control. This was confirmed by a 25% (P < .0001) decrease in pAKT ex vivo and a decrease in tumor growth. Treatment of the orthotopic xenograft with varying doses of erlotinib (25, 50, and 100 mg/kg) revealed a dose- and time-dependent increase in reporter activity (10-, 12-, and 23-fold). Correspondingly, a decrease in phospho-AKT levels (0%, 16%, and 28%, respectively) and a decrease in the AKT dependent proliferation marker PCNA (0%, 50%, and 50%) were observed. We applied μ-CT imaging for noninvasive longitudinal quantification of lung tumor load which revealed a corresponding decrease in tumor growth in a dose-dependent manner. These findings demonstrate the utility of BAR to noninvasively monitor AKT activity in preclinical studies in response to AKT modulating agents. These results also demonstrate that BAR can be applied to study drug dosing, drug combinations, and treatment efficacy in orthotopic mouse lung tumor models.

Quantifying not only bone loss, but also soft tissue swelling, in a murine inflammatory arthritis model using micro-computed tomography

In rodent models of inflammatory arthritis, bone erosion has been non-invasively assessed by micro-computed tomography (micro-CT). However, non-invasive assessments of paw swelling (oedema) are still based on clinical grading by visual evaluation, or measurements by callipers, not always reliable for the tiny mouse paws. The aim of this work was to demonstrate a novel straightforward 3D micro-CT analysis protocol capable of quantifying not only joint bone erosion, but also soft tissue swelling, from the same scans, in a rodent inflammatory arthritis model. Balb/c mice were divided into two groups: collagen antibody-induced arthritis (CAIA) and CAIA treated with prednisolone, the latter reflecting an established treatment in human rheumatoid arthritis. Clinical paw scores were recorded. On day 10, front paws were assessed by micro-CT and histology. Micro-CT measurements included paw volume (bone and soft tissue together) and bone volume at the radiocarpal joint, and bone volume from the radiocarpal to the metacarpophalangeal joint. Micro-CT analysis revealed significantly lower paw volume (−36%, P < 0.01) and higher bone volume (+17%, P < 0.05) in prednisolone-treated CAIA mice compared with untreated CAIA mice. Paw volume and bone volume assessed by micro-CT correlated significantly with clinical and histological scores (|r| > 0.5, P < 0.01). Untreated CAIA mice showed significantly higher clinical scores, higher inflammation levels histologically, cartilage and bone degradation, and pannus formation, compared with treated mice (P < 0.01). The presented novel micro-CT analysis protocol enables 3D-quantification of paw swelling at the micrometre level, along with the typically assessed bone erosion, using the same images/scans, without altering the scanning procedure or using contrast agents.

Development and optimization of a high-throughput micro-computed tomography imaging method incorporating a novel analysis technique to evaluate bone mineral density of arthritic joints in a rodent model of collagen induced arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease resulting in joint inflammation, pain, and eventual bone loss. Bone loss and remodeling caused by symmetric polyarthritis, the hallmark of RA, is readily detectable by bone mineral density (BMD) measurement using micro-CT. Abnormalities in these measurements over time reflect the underlying pathophysiology of the bone. To evaluate the efficacy of anti-rheumatic agents in animal models of arthritis, we developed a high throughput knee and ankle joint imaging assay to measure BMD as a translational biomarker. A bone sample holder was custom designed for micro-CT scanning, which significantly increased assay throughput. Batch processing 3-dimensional image reconstruction, followed by automated image cropping, significantly reduced image processing time. In addition, we developed a novel, automated image analysis method to measure BMD and bone volume of knee and ankle joints. These improvements significantly increased the throughput of ex vivo bone sample analysis, reducing data turnaround from 5 days to 24 hours for a study with 200 rat hind limbs. Taken together, our data demonstrate that BMD, as quantified by micro-CT, is a robust efficacy biomarker with a high degree of sensitivity. Our innovative approach toward evaluation of BMD using optimized image acquisition and novel image processing techniques in preclinical models of RA enables high throughput assessment of anti-rheumatic agents offering a powerful tool for drug discovery.

Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis

Genetically engineered mouse models (GEMMs) of lung cancer closely recapitulate the human disease but suffer from the difficulty of evaluating tumor growth by conventional methods. Herein, a novel automated image analysis method for estimating the lung tumor burden from in vivo micro-computed tomography (micro-CT) data is described. The proposed tumor burden metric is the segmented soft tissue volume contained within a chest space region of interest, excluding an estimate of the heart volume. The method was validated by comparison with previously published manual analysis methods and applied in two therapeutic studies in a mutant K-ras GEMM of non-small cell lung carcinoma. Mice were imaged by micro-CT pre-treatment and stratified into four treatment groups: an antibody inhibiting vascular endothelial growth factor (anti-VEGF), chemotherapy, combination of anti-VEGF and chemotherapy, or control antibody. In the first study, post-treatment imaging was performed 4 weeks later. In the second study, mice were scanned serially on a high-throughput scanner every 2 weeks for 8 weeks during treatment. In both studies, the automated tumor burden estimates were well correlated with manual metrics (r value range: 0.83-0.93, P < .0001) and showed a similar, significant reduction in tumor growth in mice treated with anti-VEGF alone or in combination with chemotherapy. Given the fully automated nature of this technique, the proposed analysis method can provide a valuable tool in preclinical drug research for screening and randomizing animals into treatment groups and evaluating treatment efficacy in mouse models of lung cancer in a highly robust and efficient manner.

Phosphorylation of FADD by the kinase CK1α promotes KRASG12D-induced lung cancer

Genomic amplification of the gene encoding and phosphorylation of the protein FADD (Fas-associated death domain) is associated with poor clinical outcome in lung cancer and in head and neck cancer. Activating mutations in the guanosine triphosphatase RAS promotes cell proliferation in various cancers. Increased abundance of phosphorylated FADD in patient-derived tumor samples predicts poor clinical outcome. Using immunohistochemistry analysis and in vivo imaging of conditional mouse models of KRAS(G12D)-driven lung cancer, we found that the deletion of the gene encoding FADD suppressed tumor growth, reduced the proliferative index of cells, and decreased the activation of downstream effectors of the RAS-MAPK (mitogen-activated protein kinase) pathway that promote the cell cycle, including retinoblastoma (RB) and cyclin D1. In mouse embryonic fibroblasts, the induction of mitosis upon activation of KRAS required FADD and the phosphorylation of FADD by CK1α (casein kinase 1α). Deleting the gene encoding CK1α in KRAS mutant mice abrogated the phosphorylation of FADD and suppressed lung cancer development. Phosphorylated FADD was most abundant during the G2/M phase of the cell cycle, and mass spectrometry revealed that phosphorylated FADD interacted with kinases that mediate the G2/M transition, including PLK1 (Polo-like kinase 1), AURKA (Aurora kinase A), and BUB1 (budding uninhibited by benzimidazoles 1). This interaction was decreased in cells treated with a CKI-7, a CK1α inhibitor. Therefore, as the kinase that phosphorylates FADD downstream of RAS, CK1α may be a therapeutic target for KRAS-driven lung cancer.

Anti-inflammatory actions of Chemoattractant Receptor-homologous molecule expressed on Th2 by the antagonist MK-7246 in a novel rat model of Alternaria alternata elicited pulmonary inflammation

Alternaria alternata is a fungal allergen linked to the development of severe asthma in humans. In view of the clinical relationship between A. alternata and asthma, we sought to investigate the allergic activity of this antigen after direct application to the lungs of Brown Norway rats. Here we demonstrate that a single intratracheal instillation of A. alternata induces dose and time dependent eosinophil influx, edema and Type 2 helper cell cytokine production in the lungs of BN rats. We established the temporal profile of eosinophilic infiltration and cytokine production, such as Interleukin-5 and Interleukin-13, following A. alternata challenge. These responses were comparable to Ovalbumin induced models of asthma and resulted in peak inflammatory responses 48h following a single challenge, eliminating the need for multiple sensitizations and challenges. The initial perivascular and peribronchiolar inflammation preceded alveolar inflammation, progressing to a more sub-acute inflammatory response with notable epithelial cell hypertrophy. To limit the effects of an A. alternata inflammatory response, MK-7246 was utilized as it is an antagonist for Chemoattractant Receptor-homologous molecule expressed in Th2 cells. In a dose-dependent manner, MK-7246 decreased eosinophil influx and Th2 cytokine production following the A. alternata challenge. Furthermore, therapeutic administration of corticosteroids resulted in a dose-dependent decrease in eosinophil influx and Th2 cytokine production. Reproducible asthma-related outcomes and amenability to pharmacological intervention by mechanisms relevant to asthma demonstrate that an A. alternata induced pulmonary inflammation in BN rats is a valuable preclinical pharmacodynamic in vivo model for evaluating the pharmacological inhibitors of allergic pulmonary inflammation.

Growth pattern analysis of murine lung neoplasms by advanced semi-automated quantification of micro-CT images

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses.

Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model

We present and evaluate an automatic and quantitative method for the complex task of characterizing individual nodule volumetric progression in a longitudinal mouse model of lung cancer. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were acquired at 8, 22, and 37 weeks after injection. A radiologist identified a total of 196, 585 and 636 nodules, respectively. The three micro-CT image volumes from every animal were then registered and the nodules automatically matched with an average accuracy of 99.5%. All nodules detected at week 8 were tracked all the way to week 37, and volumetrically segmented to measure their growth and doubling rates. 92.5% of all nodules were correctly segmented, ranging from the earliest stage to advanced stage, where nodule segmentation becomes more challenging due to complex anatomy and nodule overlap. Volume segmentation was validated using a foam lung phantom with embedded polyethylene microspheres. We also correlated growth rates with nodule phenotypes based on histology, to conclude that the growth rate of malignant tumors is significantly higher than that of benign lesions. In conclusion, we present a turnkey solution that combines longitudinal imaging with nodule matching and volumetric nodule segmentation resulting in a powerful tool for preclinical research.

Contrast agents for quantitative microCT of lung tumors in mice

The identification and quantitative evaluation of lung tumors in mouse models is challenging and an unmet need in preclinical arena. In this study, we developed a noninvasive contrast-enhanced microCT (μCT) method to longitudinally evaluate and quantitate lung tumors in mice. Commercially available μCT contrast agents were compared to determine the optimal agent for visualization of thoracic blood vessels and lung tumors in naïve mice and in non-small-cell lung cancer models. Compared with the saline control, iopamidol and iodinated lipid agents provided only marginal increases in contrast resolution. The inorganic nanoparticulate agent provided the best contrast and visualization of thoracic vascular structures; the density contrast was highest at 15 min after injection and was stable for more than 4 h. Differential contrast of the tumors, vascular structures, and thoracic air space by the nanoparticulate agent enabled identification of tumor margins and accurate quantification. μCT data correlated closely with traditional histologic measurements (Pearson correlation coefficient, 0.995). Treatment of ELM4-ALK mice with crizotinib yielded 65% reduction in tumor size and thus demonstrated the utility of quantitative μCT in longitudinal preclinical trials. Overall and among the 3 agents we tested, the inorganic nanoparticulate product was the best commercially available contrast agent for visualization of thoracic blood vessels and lung tumors. Contrast-enhanced μCT imaging is an excellent noninvasive method for longitudinal evaluation during preclinical lung tumor studies.

IDO is a nodal pathogenic driver of lung cancer and metastasis development

Indoleamine 2,3-dioxygenase (IDO) enzyme inhibitors have entered clinical trials for cancer treatment based on preclinical studies, indicating that they can defeat immune escape and broadly enhance other therapeutic modalities. However, clear genetic evidence of the impact of IDO on tumorigenesis in physiologic models of primary or metastatic disease is lacking. Investigating the impact of Ido1 gene disruption in mouse models of oncogenic KRAS-induced lung carcinoma and breast carcinoma-derived pulmonary metastasis, we have found that IDO deficiency resulted in reduced lung tumor burden and improved survival in both models. Micro-computed tomographic (CT) imaging further revealed that the density of the underlying pulmonary blood vessels was significantly reduced in Ido1-nullizygous mice. During lung tumor and metastasis outgrowth, interleukin (IL)-6 induction was greatly attenuated in conjunction with the loss of IDO. Biologically, this resulted in a consequential impairment of protumorigenic myeloid-derived suppressor cells (MDSC), as restoration of IL-6 recovered both MDSC suppressor function and metastasis susceptibility in Ido1-nullizygous mice. Together, our findings define IDO as a prototypical integrative modifier that bridges inflammation, vascularization, and immune escape to license primary and metastatic tumor outgrowth.

SIGNIFICANCE

This study provides preclinical, genetic proof-of-concept that the immunoregulatory enzyme IDO contributes to autochthonous carcinoma progression and to the creation of a metastatic niche. IDO deficiency in vivo negatively impacted both vascularization and IL-6–dependent, MDSC-driven immune escape, establishing IDO as an overarching factor directing the establishment of a protumorigenic environment.

Anti-VEGF antibody therapy does not promote metastasis in genetically engineered mouse tumour models

Resistance to anti-angiogenic therapy can occur via several potential mechanisms. Unexpectedly, recent studies showed that short-term inhibition of either VEGF or VEGFR enhanced tumour invasiveness and metastatic spread in preclinical models. In an effort to evaluate the translational relevance of these findings, we examined the consequences of long-term anti-VEGF monoclonal antibody therapy in several well-validated genetically engineered mouse tumour models of either neuroendocrine or epithelial origin. Anti-VEGF therapy decreased tumour burden and increased overall survival, either as a single agent or in combination with chemotherapy, in all four models examined. Importantly, neither short- nor long-term exposure to anti-VEGF therapy altered the incidence of metastasis in any of these autochthonous models, consistent with retrospective analyses of clinical trials. In contrast, we observed that sunitinib treatment recapitulated previously reported effects on tumour invasiveness and metastasis in a pancreatic neuroendocrine tumour (PNET) model. Consistent with these results, sunitinib treatment resulted in an up-regulation of the hypoxia marker GLUT1 in PNETs, whereas anti-VEGF did not. These results indicate that anti-VEGF mediates anti-tumour effects and therapeutic benefits without a paradoxical increase in metastasis. Moreover, these data underscore the concept that drugs targeting VEGF ligands and receptors may affect tumour metastasis in a context-dependent manner and are mechanistically distinct from one another.

The interconnectedness of cancer cell signaling

The elegance of fundamental and applied research activities have begun to reveal a myriad of spatial and temporal alterations in downstream signaling networks affected by cell surface receptor stimulation including G protein-coupled receptors and receptor tyrosine kinases. Interconnected biochemical pathways serve to integrate and distribute the signaling information throughout the cell by orchestration of complex biochemical circuits consisting of protein interactions and covalent modification processes. It is clear that scientific literature summarizing results from both fundamental and applied scientific research activities has served to provide a broad foundational biologic database that has been instrumental in advancing our continued understanding of underlying cancer biology. This article reflects on historical advances and the role of innovation in the competitive world of grant-sponsored research.

Dinosaurs and ancient civilizations: reflections on the treatment of cancer

Research efforts in the area of palaeopathology have been seen as an avenue to improve our understanding of the pathogenesis of cancer. Answers to questions of whether dinosaurs had cancer, or if cancer plagued ancient civilizations, have captured the imagination as well as the popular media. Evidence for dinosaurian cancer may indicate that cancer may have been with us from the dawn of time. Ancient recorded history suggests that past civilizations attempted to fight cancer with a variety of interventions. When contemplating the issue why a generalized cure for cancer has not been found, it might prove useful to reflect on the relatively limited time that this issue has been an agenda item of governmental attention as well as continued introduction of an every evolving myriad of manmade carcinogens relative to the total time cancer has been present on planet Earth. This article reflects on the history of cancer and the progress made following the initiation of the "era of cancer chemotherapy."

Silibinin prevents lung tumorigenesis in wild-type but not in iNOS-/- mice: potential of real-time micro-CT in lung cancer chemoprevention studies

PURPOSE

Sustained nitric oxide (NO) generation positively correlates with lung cancer development and progression. Herein, we genetically confirmed this role of iNOS and evaluated the chemopreventive efficacy of silibinin in carcinogen-treated B6/129 wild-type (WT) and iNOS(-/-) mice.

EXPERIMENTAL DESIGN

Male B6/129-Nos2(tm1Lau) (iNOS(-/-)) and B6/129PF2 WT mice were injected i.p. with 1 mg/g body weight urethane once weekly for 7 consecutive weeks, followed by silibinin gavage (742 mg/kg body weight) for 5 d/wk for 18 weeks.

RESULTS

Quantification of micro-CT data in real-time showed that silibinin significantly decreases urethane-induced tumor number and size in WT mice, consistent with measurements made ex vivo at study termination. Genetic ablation of iNOS decreased urethane-induced tumor multiplicity by 87% (P < 0.001) compared to WT mice. Silibinin decreased tumor multiplicity by 71% (P < 0.01) in WT mice, but did not show any such considerable effect in iNOS(-/-) mice. Tumors from WT mice expressed more iNOS (P < 0.01) but almost similar eNOS and nNOS than those in silibinin-treated mice. In these tumors, silibinin moderately (P < 0.01) inhibited cell proliferation but strongly (P < 0.01) reduced the number of newly formed nestin-positive microvessels. Silibinin decreased VEGFR2 level, and STAT3 and NF-κB activation in tumors.

CONCLUSIONS

The lack of effect of silibinin in iNOS(-/-) mice suggests that silibinin exerts most of its chemopreventive and angiopreventive effects through its inhibition of iNOS expression in lung tumors. Our results support iNOS as a potential target for controlling lung cancer, and demonstrate the value of real-time noninvasive micro-CT imaging modality for evaluating the efficacy of lung cancer chemopreventive agents.

Phantom and cadaver measurements of dose and dose distribution in micro-CT of the chest in mice

BACKGROUND

Micro-computed tomography (CT) allows high-resolution imaging of the chest in mice for small animal research with a significant radiation dose applied.

PURPOSE

To report on measurement of the applied radiation dose using different scan protocols in micro-CT of the chest in mice.

MATERIAL AND METHODS

Repetitive dose measurements were performed for four different micro-CT protocols (with/without respiratory gating) and for micro-CT fluoroscopy used for chest imaging. Measurements were carried out using thermoluminescence dosimeters (TLD) in mouse cadavers and in a PMMA phantom allowing measurement of the radiation dose in the direct path of rays and assessment of scattered radiation.

RESULTS

The dose measured inside and outside the chests of the cadavers varied between 190 und 210 mGy, respectively. The expected mean doses in mice in the direct path of rays for the four examined micro-CT protocols varied between 170 and 280 mGy. The mean values for 1 and 5 minutes of fluoroscopy were 17 mGy and 105 mGy, respectively.

CONCLUSION

The measured dose values are similar to the dose values for micro-CT of the chest reported so far. A relevant dose can be delivered by micro-CT of the chest, which could possibly interact with small animal studies. Therefore, the applied dose for a specific protocol should be known and adverse radiation effects be considered.

MET kinase inhibitor SGX523 synergizes with epidermal growth factor receptor inhibitor erlotinib in a hepatocyte growth factor-dependent fashion to suppress carcinoma growth

The hepatocyte growth factor (HGF)-MET pathway supports several hallmark cancer traits, and it is frequently activated in a broad spectrum of human cancers (http://www.vai.org/met/). With the development of many cancer drugs targeting this pathway, there is a need for relevant in vivo model systems for preclinical evaluation of drug efficacy. Here, we show that production of the human HGF ligand in transgenic severe combined immunodeficient mice (hHGF(tg)-SCID mice) enhances the growth of many MET-expressing human carcinoma xenografts, including those derived from lung, breast, kidney, colon, stomach, and pancreas. In this model, the MET-specific small-molecule kinase inhibitor SGX523 partially inhibits the HGF-dependent growth of lung, breast, and pancreatic tumors. However, much greater growth suppression is achieved by combinatorial inhibition with the epidermal growth factor receptor (EGFR) kinase inhibitor erlotinib. Together, these results validate the hHGF(tg)-SCID mouse model for in vivo determination of MET sensitivity to drug inhibition. Our findings also indicate that simultaneously targeting the MET and EGFR pathways can provide synergistic inhibitory effects for the treatment of cancers in which both pathways are activated.

The War on Cancer rages on

In 1971, the "War on Cancer" was launched by the US government to cure cancer by the 200-year anniversary of the founding of the United States of America, 1976. This article briefly looks back at the progress that has been made in cancer research and compares progress made in other areas of human affliction. While progress has indeed been made, the battle continues to rage on.