Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients.

INTRODUCTION: Spinal measurements play an integral role in surgical planning for a variety of spine procedures. Full-length imaging eliminates distortions that can occur with stitched images. However, these images take radiologists significantly longer to read than conventional radiographs. Artificial intelligence (AI) image analysis software that can make such measurements quickly and reliably would be advantageous to surgeons, radiologists, and the entire health system. MATERIALS AND METHODS: Institutional Review Board approval was obtained for this study. Preoperative full-length standing anterior-posterior and lateral radiographs of patients that were previously measured by fellowship-trained spine surgeons at our institution were obtained. The measurements included lumbar lordosis (LL), greatest coronal Cobb angle (GCC), pelvic incidence (PI), coronal balance (CB), and T1-pelvic angle (T1PA). Inter-rater intra-class correlation (ICC) values were calculated based on an overlapping sample of 10 patients measured by surgeons. Full-length standing radiographs of an additional 100 patients were provided for AI software training. The AI algorithm then measured the radiographs and ICC values were calculated. RESULTS: ICC values for inter-rater reliability between surgeons were excellent and calculated to 0.97 for LL (95% CI 0.88-0.99), 0.78 (0.33-0.94) for GCC, 0.86 (0.55-0.96) for PI, 0.99 for CB (0.93-0.99), and 0.95 for T1PA (0.82-0.99). The algorithm computed the five selected parameters with ICC values between 0.70 and 0.94, indicating excellent reliability. Exemplary for the comparison of AI and surgeons, the ICC for LL was 0.88 (95% CI 0.83-0.92) and 0.93 for CB (0.90-0.95). GCC, PI, and T1PA could be determined with ICC values of 0.81 (0.69-0.87), 0.70 (0.60-0.78), and 0.94 (0.91-0.96) respectively. CONCLUSIONS: The AI algorithm presented here demonstrates excellent reliability for most of the parameters and good reliability for PI, with ICC values corresponding to measurements conducted by experienced surgeons. In future, it may facilitate the analysis of large data sets and aid physicians in diagnostics, pre-operative planning, and post-operative quality control.

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). MATERIAL AND METHODS: Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. RESULTS: The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. CONCLUSION: This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.

Novel AI-Based Algorithm for the Automated Measurement of Cervical Sagittal Balance Parameters. A Validation Study on Pre- and Postoperative Radiographs of 129 Patients.

STUDY DESIGN: Retrospective, mono-centric cohort research study. OBJECTIVES: The analysis of cervical sagittal balance parameters is essential for preoperative planning and dependent on the physician's experience. A fully automated artificial intelligence-based algorithm could contribute to an objective analysis and save time. Therefore, this algorithm should be validated in this study. METHODS: Two surgeons measured C2-C7 lordosis, C1-C7 Sagittal Vertical Axis (SVA), C2-C7-SVA, C7-slope and T1-slope in pre- and postoperative lateral cervical X-rays of 129 patients undergoing anterior cervical surgery. All parameters were measured twice by surgeons and compared to the measurements by the AI algorithm consisting of 4 deep convolutional neural networks. Agreement between raters was quantified, among other metrics, by mean errors and single measure intraclass correlation coefficients for absolute agreement. RESULTS: ICC-values for intra- (range: .92-1.0) and inter-rater (.91-1.0) reliability reflect excellent agreement between human raters. The AI-algorithm could determine all parameters with excellent ICC-values (preop:0.80-1.0; postop:0.86-.99). For a comparison between the AI algorithm and 1 surgeon, mean errors were smallest for C1-C7 SVA (preop: -.3 mm (95% CI:-.6 to -.1 mm), post: .3 mm (.0-.7 mm)) and largest for C2-C7 lordosis (preop:-2.2° (-2.9 to -1.6°), postop: 2.3°(-3.0 to -1.7°)). The automatic measurement was possible in 99% and 98% of pre- and postoperative images for all parameters except T1 slope, which had a detection rate of 48% and 51% in pre- and postoperative images. CONCLUSION: This study validates that an AI-algorithm can reliably measure cervical sagittal balance parameters automatically in patients suffering from degenerative spinal diseases. It may simplify manual measurements and autonomously analyze large-scale datasets. Further studies are required to validate the algorithm on a larger and more diverse patient cohort.

Accurate determination of hip implant wear, cup anteversion and inclination through AI automated 2D-3D registration.

The precise and accurate measurement of implant wear, acetabular cup anteversion and inclination from routine anterior-posterior radiographs still poses a challenge. Current approaches suffer from time-consuming procedures accompanied by low and observer-dependent accuracy and precision. We present and validate a novel, automated method for determining total hip arthroplasty parameters by comparing its accuracy and precision with methods in contemporary scientific literature. The algorithm uses CAD-model-based two dimensional-three dimensional (2D-3D)-registration supported by convolutional neural networks. Two in-vitro experimental set-ups were designed to validate the proposed 2D-3D-method. The set-ups provided 84 predefined wear values and 24 configurations of anteversion and inclination in 114 radiographs. Accuracy and precision were evaluated by systematically comparing the predefined ground truth and the automatically calculated values from in-vitro X-rays. In addition, an algorithm was developed and validated against physician's measurements on clinical X-rays to determine the inclination of the interteardrop (ITL) and biischial line (BL) to account for the individual patient's pelvic rotation in the frontal plane. Using X-rays from experimental set-ups, the determined mean error was 0.014 mm (standard deviation: 0.020 mm; root-mean-square-error: 0.024 mm) for wear in pelvic position, -0.01° (0.24°; 0.23°) for radiographic cup anteversion, and 0.11° (0.38°; 0.39°) for radiographic cup inclination. The inclination of ITL and BL was automatically determined in all clinical X-rays with excellent interclass correlation coefficients of 0.95 and 0.91, respectively. The presented algorithm allows the accurate and precise evaluation of total hip arthroplasty parameters without additional equipment. The method might help to investigate different implant designs, biomaterials, and surgical techniques with greater objectivity.

Identification of radiation responsive RBC membrane associated proteins (RMAPs) in whole-body γ-irradiated New Zealand white rabbits.

This study is aimed to identify radiation-responsive RBC Membrane Associated Proteins (RMAPs) in Rabbits in vivo. Male New Zealand White rabbits were exposed to a single acute total body γ-radiation dose of 2 Gy at a dose rate of 0.746 Gy/min. Following this, at early time points of 6 h till the 7 d, RMAPs were collected and analyzed by MALDI-TOF-MS. Bioinformatics analysis was conducted to explore the biological functions of these proteins. Based on fold change, radiation responsiveness, GO, pathway enrichment, and hub position in the PPI network, we identified seven RMAPs as potential biomarker candidates viz., PVALB, PRKCB, GPD1, CP2G1, CSNK2B, ATP1B1, TPI1. As per KEGG enrichment, most of the proteins were implicated in cellular radiation response, oxidative damage, DNA repair, apoptosis, immune response, and cell signaling. This study forms the foundation for RMAPs-based Proteomic strategies for high throughput radiation bio-dosimetry for triage in the case of a radiological/nuclear incident.

Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.

STUDY DESIGN: Retrospective, mono-centric cohort research study. OBJECTIVES: The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS). METHODS: An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics. RESULTS: The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°). CONCLUSIONS: The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.

Automated Artificial Intelligence-Based Assessment of Lower Limb Alignment Validated on Weight-Bearing Pre- and Postoperative Full-Leg Radiographs.

The assessment of the knee alignment using standing weight-bearing full-leg radiographs (FLR) is a standardized method. Determining the load-bearing axis of the leg requires time-consuming manual measurements. The aim of this study is to develop and validate a novel algorithm based on artificial intelligence (AI) for the automated assessment of lower limb alignment. In the first stage, a customized mask-RCNN model was trained to automatically detect and segment anatomical structures and implants in FLR. In the second stage, four region-specific neural network models (adaptations of UNet) were trained to automatically place anatomical landmarks. In the final stage, this information was used to automatically determine five key lower limb alignment angles. For the validation dataset, weight-bearing, antero-posterior FLR were captured preoperatively and 3 months postoperatively. Preoperative images were measured by the operating orthopedic surgeon and an independent physician. Postoperative images were measured by the second rater only. The final validation dataset consisted of 95 preoperative and 105 postoperative FLR. The detection rate for the different angles ranged between 92.4% and 98.9%. Human vs. human inter-(ICCs: 0.85−0.99) and intra-rater (ICCs: 0.95−1.0) reliability analysis achieved significant agreement. The ICC-values of human vs. AI inter-rater reliability analysis ranged between 0.8 and 1.0 preoperatively and between 0.83 and 0.99 postoperatively (all p < 0.001). An independent and external validation of the proposed algorithm on pre- and postoperative FLR, with excellent reliability for human measurements, could be demonstrated. Hence, the algorithm might allow for the objective and time saving analysis of large datasets and support physicians in daily routine.

Can artificial intelligence support or even replace physicians in measuring sagittal balance? A validation study on preoperative and postoperative full spine images of 170 patients.

PURPOSE: Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation. METHODS: Pre- and postoperative sagittal full body radiographs of 170 patients were measured by two human raters, twice by one rater and by the AI algorithm which determined: pelvic incidence, pelvic tilt, sacral slope, L1-S1 lordosis, T4-T12 thoracic kyphosis (TK) and the spino-sacral angle (SSA). To evaluate the agreement between human raters and AI, the mean error (95% confidence interval (CI)), standard deviation and an intra- and inter-rater reliability was conducted using intra-class correlation (ICC) coefficients. RESULTS: ICC values for the assessment of the intra- (range: 0.88-0.97) and inter-rater (0.86-0.97) reliability of human raters are excellent. The algorithm is able to determine all parameters in 95% of all pre- and in 91% of all postoperative images with excellent ICC values (PreOP-range: 0.83-0.91, PostOP: 0.72-0.89). Mean errors are smallest for the SSA (PreOP: -0.1° (95%-CI: -0.9°-0.6°); PostOP: -0.5° (-1.4°-0.4°)) and largest for TK (7.0° (6.1°-7.8°); 7.1° (6.1°-8.1°)). CONCLUSION: A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from time-consuming routine work of measuring SB parameters and allow the analysis of large databases efficiently.

Novel artificial intelligence algorithm: an accurate and independent measure of spinopelvic parameters.

OBJECTIVE: The analysis of sagittal alignment by measuring spinopelvic parameters has been widely adopted among spine surgeons globally, and sagittal imbalance is a well-documented cause of poor quality of life. These measurements are time-consuming but necessary to make, which creates a growing need for an automated analysis tool that measures spinopelvic parameters with speed, precision, and reproducibility without relying on user input. This study introduces and evaluates an algorithm based on artificial intelligence (AI) that fully automatically measures spinopelvic parameters. METHODS: Two hundred lateral lumbar radiographs (pre- and postoperative images from 100 patients undergoing lumbar fusion) were retrospectively analyzed by board-certified spine surgeons who digitally measured lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope. The novel AI algorithm was also used to measure the same parameters. To evaluate the agreement between human and AI-automated measurements, the mean error (95% CI, SD) was calculated and interrater reliability was assessed using the 2-way random single-measure intraclass correlation coefficient (ICC). ICC values larger than 0.75 were considered excellent. RESULTS: The AI algorithm determined all parameters in 98% of preoperative and in 95% of postoperative images with excellent ICC values (preoperative range 0.85-0.92, postoperative range 0.81-0.87). The mean errors were smallest for pelvic incidence both pre- and postoperatively (preoperatively -0.5° [95% CI -1.5° to 0.6°] and postoperatively 0.0° [95% CI -1.1° to 1.2°]) and largest preoperatively for sacral slope (-2.2° [95% CI -3.0° to -1.5°]) and postoperatively for lumbar lordosis (3.8° [95% CI 2.5° to 5.0°]). CONCLUSIONS: Advancements in AI translate to the arena of medical imaging analysis. This method of measuring spinopelvic parameters on spine radiographs has excellent reliability comparable to expert human raters. This application allows users to accurately obtain critical spinopelvic measurements automatically, which can be applied to clinical practice. This solution can assist physicians by saving time in routine work and by avoiding error-prone manual measurements.

Overexpression of MUC1 Induces Non-Canonical TGF-ß Signaling in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDA) is one of the most lethal human cancers. Transforming Growth Factor Beta (TGF-ß) is a cytokine that switches from a tumor-suppressor at early stages to a tumor promoter in the late stages of tumor development, by yet unknown mechanisms. Tumor associated MUC1 is aberrantly glycosylated and overexpressed in >80% of PDAs and is associated with poor prognosis. MUC1 expression is found in the early stages of PDA development with subsequent increase in later stages. Analysis of human PDA samples from TCGA database showed significant differences in gene expression and survival profiles between low and high MUC1 samples. Further, high MUC1 expression was found to positively correlate to TGF-ßRII expression and negatively correlate to TGF-ßRI expression in PDA cell lines. We hypothesized that MUC1 overexpression induces TGF-ß mediated non-canonical signaling pathways which is known to be associated with poor prognosis. In this study, we report that MUC1 overexpression in PDA cells directly activates the JNK pathway in response to TGF-ß, and leads to increased cell viability via up-regulation and stabilization of c-Myc. Conversely, in low MUC1 expressing PDA cells, TGF-ß preserves its tumor-suppressive function and inhibits phosphorylation of JNK and stabilization of c-Myc. Knockdown of MUC1 in PDA cells also results in decreased phosphorylation of JNK and c-Myc in response to TGF-ß treatment. Taken together, the results indicate that overexpression of MUC1 plays a significant role in switching the TGF-ß function from a tumor-suppressor to a tumor promoter by directly activating JNK. Lastly, we report that high-MUC1 PDA tumors respond to TGF-ß neutralizing antibody in vivo showing significantly reduced tumor growth while low-MUC1 tumors do not respond to TGF-ß neutralizing antibody further confirming our hypothesis.

In vivo-wound healing studies of sodium thiosulfate gel in rats.

Sodium Thiosulfate (STS) is already reported as an antioxidant, anti-inflammatory agent with antiseptic, antifungal properties. The search for an ideal antiseptic still continues, which is lethal to all types of bacteria and their spores and sustain the activity for a longer time without any harm to the host tissue. The aim of the present study is to evaluate the effect of STS on curing of wounds in rats when compared to Betadine. We developed topical gels having 6% and 12% STS. The effects of STS on wound healing rate of Rats were evaluated against Betadine as positive control. Wounds of control group, selected as Group 1 was treated with normal saline (0.2 ml), twice a day. Reference standard control, designated as Group 2 rats were given with 0.2 ml Betadine twice a day. Rats in Groups 3 and 4 were treated with 0.2 ml of STS gel (6% or 12% respectively) twice a day. In our study, STS formulation has proved to be a safe and efficient wound healing product. It has a neutral pH and longer half life (>12 months). Higher STS dose of 12% proved to have a wound curing rate equivalent to that of Betadine. On 11th Day, 97 ± 0.79% healing was achieved with Betadine and 98 ± 0.67% with 12% STS Gel (∗P < 0.05). Microscopic images of H&E stained skin tissue from animals treated with Betadine and 12% STS formulation showed a reduction in scar size, lesser amount of inflammatory cells, higher fibroblasts and blood vessels, with considerable collagen accumulation. Furthermore, a significant enhancement in the levels of GPx, CAT and SOD was observed in the tissue at the wound site of the treated group. The IL 10 levels in both groups of STS-treated rats was increased, whereas, TNF-α levels were reduced significantly in tissue homogenate compared with control. Thus, this study shows the wound-healing performance of STS formulation. Further studies are necessary to understand the real mechanism of how STS formulation heals wounds.

C-Phycocyanin-a novel protein from Spirulina platensis- In vivo toxicity, antioxidant and immunomodulatory studies.

A pigment-protein highly dominant in Spirulina is known as C-Phycocyanin. Earlier, in vitro studies has shown that C-phycocyanin is having many biological activities like antioxidant and anti-inflammatory activities, antiplatelet, hepatoprotective, and cholesterol-lowering properties. Interestingly, there are scanty in vivo experimental findings on the immunomodulatory and antioxidant effects of C-phycocyanin. This work is aimed at in vivo evaluation of the effects of C-phycocyanin on immunomodulation and antioxidant potential in Balb/c mice. Our results of in vivo toxicity, immunomodulatory and antioxidant effects of C-Phycocyanin suggests that C-phycocyanin is very safe for consumption and having substantial antioxidant potential and also possess immunomodulatory activities in Balb/c mice in a dosage dependent manner. C-phycocyanin doesn't cause acute and subacute toxicity in the animal model (male, Balb/c mice) studied. We have reported that C-phycocyanin exhibited in vivo immunomodulation performance in this animal model.

Indomethacin enhances anti-tumor efficacy of a MUC1 peptide vaccine against breast cancer in MUC1 transgenic mice.

In recent years, vaccines against tumor antigens have shown potential for combating invasive cancers, including primary tumors and metastatic lesions. This is particularly pertinent for breast cancer, which is the second-leading cause of cancer-related death in women. MUC1 is a glycoprotein that is normally expressed on glandular epithelium, but is overexpressed and under-glycosylated in most human cancers, including the majority of breast cancers. This under-glycosylation exposes the MUC1 protein core on the tumor-associated form of the protein. We have previously shown that a vaccine consisting of MUC1 core peptides stimulates a tumor-specific immune response. However, this immune response is dampened by the immunosuppressive microenvironment within breast tumors. Thus, in the present study, we investigated the effectiveness of MUC1 vaccination in combination with four different drugs that inhibit different components of the COX pathway: indomethacin (COX-1 and COX-2 inhibitor), celecoxib (COX-2 inhibitor), 1-methyl tryptophan (indoleamine 2,3 dioxygenase inhibitor), and AH6809 (prostaglandin E2 receptor antagonist). These treatment regimens were explored for the treatment of orthotopic MUC1-expressing breast tumors in mice transgenic for human MUC1. We found that the combination of vaccine and indomethacin resulted in a significant reduction in tumor burden. Indomethacin did not increase tumor-specific immune responses over vaccine alone, but rather appeared to reduce the proliferation and increase apoptosis of tumor cells, thus rendering them susceptible to immune cell killing.

Overcoming Immunological Resistance Enhances the Efficacy of A Novel Anti-tMUC1-CAR T Cell Treatment against Pancreatic Ductal Adenocarcinoma.

Chimeric antigen receptor (CAR) T cells have shown remarkable success in treating hematologic cancers. However, this efficacy has yet to translate to treatment in solid tumors. Pancreatic ductal adenocarcinoma (PDA) is a fatal malignancy with poor prognosis and limited treatment options. We have developed a second generation CAR T cell using the variable fragments of a novel monoclonal antibody, TAB004, which specifically binds the tumor-associated-MUC1 (tMUC1). tMUC1 is overexpressed on ~85% of all human PDA. We present data showing that TAB004-derived CAR T cells specifically bind to tMUC1 on PDA cells and show robust killing activity; however, they do not bind or kill normal epithelial cells. We further demonstrated that the tMUC1-CAR T cells control the growth of orthotopic pancreatic tumors in vivo. We witnessed that some PDA cells (HPAFII and CFPAC) were refractory to CAR T cell treatment. qPCR analysis of several genes revealed overexpression of indoleamine 2, 3-dioxygenases-1 (IDO1), cyclooxygenase 1 and 2 (COX1/2), and galectin-9 (Gal-9) in resistant PDA cells. We showed that combination of CAR T cells and biological inhibitors of IDO1, COX1/2, and Gal-9 resulted in significant enhancement of CAR T cell cytotoxicity against PDA cells. Overcoming PDA resistance is a significant advancement in the field.

SMAD4-independent activation of TGF-ß signaling by MUC1 in a human pancreatic cancer cell line.

Pancreatic Ductal Adenocarcinoma (PDA) has a mortality rate that nearly matches its incidence rate. Transforming Growth Factor Beta (TGF-ß) is a cytokine with a dual role in tumor development switching from a tumor suppressor to a tumor promoter. There is limited knowledge of how TGF-ß function switches during tumorigenesis. Mucin 1 (MUC1) is an aberrantly glycosylated, membrane-bound, glycoprotein that is overexpressed in >80% of PDA cases and is associated with poor prognosis. In PDA, MUC1 promotes tumor progression and metastasis via signaling through its cytoplasmic tail (MUC1-CT) and interacting with other oncogenic signaling molecules. We hypothesize that high levels of MUC1 in PDA may be partly responsible for the TGF-ß functional switch during oncogenesis. We report that overexpression of MUC1 in BxPC3 human PDA cells (BxPC3.MUC1) enhances the induction of epithelial to mesenchymal transition leading to increased invasiveness in response to exogenous TGF-ß1. Simultaneously, these cells resist TGF-ß induced apoptosis by downregulating levels of cleaved caspases. We show that mutating the tyrosines in MUC1-CT to phenylalanine reverses the TGF-ß induced invasiveness. This suggests that the tyrosine residues in MUC1-CT are required for TGF-ß induced invasion. Some of these tyrosines are phosphorylated by the tyrosine kinase c-Src. Thus, treatment of BxPC3.MUC1 cells with a c-Src inhibitor (PP2) significantly reduces TGF-ß induced invasiveness. Similar observations were confirmed in the Chinese hamster ovarian (CHO) cell line. Data strongly suggests that MUC1 may regulate TGF-ß function in PDA cells and thus have potential clinical relevance in the use of TGF-ß inhibitors in clinical trials.

Early detection of pancreatic cancer in mouse models using a novel antibody, TAB004.

Pancreatic ductal adenocarcinoma (PDA) is the fourth-leading cause of cancer death in the United States with a 5-year overall survival rate of 8% for all stages combined. But this decreases to 3% for the majority of patients that present with stage IV PDA at time of diagnosis. The lack of distinct early symptoms for PDA is one of the primary reasons for the late diagnosis. Common symptoms like weight loss, abdominal and back pains, and jaundice are often mistaken for symptoms of other issues and do not appear until the cancer has progressed to a late stage. Thus the development of novel imaging platforms for PDA is crucial for the early detection of the disease. MUC1 is a tumor-associated antigen (tMUC1) expressed on 80% of PDA. The goal of this study was to determine the targeting and detection capabilities of a tMUC1 specific antibody, TAB004. TAB004 antibody conjugated to a near infrared fluorescent probe was injected intraperitoneally into immune competent orthotopic and spontaneous models of PDA. Results show that fluorophore conjugated TAB004 specifically targets a) 1 week old small tumor in the pancreas in an orthotopic PDA model and b) very early pre-neoplastic lesions (PanIN lesions) that develop in the spontaneous PDA model before progression to adenocarcinoma. Thus, TAB004 is a promising antibody to deliver imaging agents directly to the pancreatic tumor microenvironment, significantly affecting early detection of PDA.

Antibody-Guided In Vivo Imaging for Early Detection of Mammary Gland Tumors.

BACKGROUND: Earlier detection of transformed cells using target-specific imaging techniques holds great promise. We have developed TAB 004, a monoclonal antibody highly specific to a protein sequence accessible in the tumor form of MUC1 (tMUC1). We present data assessing both the specificity and sensitivity of TAB 004 in vitro and in genetically engineered mice in vivo. METHODS: Polyoma Middle T Antigen mice were crossed to the human MUC1.Tg mice to generate MMT mice. In MMT mice, mammary gland hyperplasia is observed between 6 and 10 weeks of age that progresses to ductal carcinoma in situ by 12 to 14 weeks and adenocarcinoma by 18 to 24 weeks. Approximately 40% of these mice develop metastasis to the lung and other organs with a tumor evolution that closely mimics human breast cancer progression. Tumor progression was monitored in MMT mice (from ages 8 to 22 weeks) by in vivo imaging following retro-orbital injections of the TAB 004 conjugated to indocyanine green (TAB-ICG). At euthanasia, mammary gland tumors and normal epithelial tissues were collected for further analyses. RESULTS: In vivo imaging following TAB-ICG injection permitted significantly earlier detection of tumors compared with physical examination. Furthermore, TAB-ICG administration in MMT mice enabled the detection of lung metastases while sparing recognition of normal epithelia. CONCLUSIONS: The data highlight the specificity and the sensitivity of the TAB 004 antibody in differentiating normal versus tumor form of MUC1 and its utility as a targeted imaging agent for early detection, tumor monitoring response, as well as potential clinical use for targeted drug delivery.

Mucin 1 Regulates Cox-2 Gene in Pancreatic Cancer.

OBJECTIVE: Eighty percent of pancreatic ductal adenocarcinomas (PDAs) overexpress mucin 1 (MUC1), a transmembrane mucin glycoprotein. MUC1(high) PDA patients also express high levels of cyclooxygenase 2 (COX-2) and show poor prognosis. The cytoplasmic tail of MUC1 (MUC1-CT) partakes in oncogenic signaling, resulting in accelerated cancer progression. Our aim was to understand the regulation of Cox-2 expression by MUC1. METHODS: Levels of COX-2 and MUC1 were determined in MUC1(-/-), MUC1(low), and MUC1(high) PDA cells and tumors using reverse transcriptase-polymerase chain reaction, Western blot, and immunohistochemistry. Proliferative and invasive potential was assessed using MTT and Boyden chamber assays. Chromatin immunoprecipitation was performed to evaluate binding of MUC1-CT to the promoter of COX-2 gene. RESULTS: Significantly higher levels of COX-2 mRNA and protein were detected in MUC1(high) versus MUC1(low/null) cells, which were recapitulated in vivo. In addition, deletion of MUC1 gene and transient knockdown of MUC1 led to decreased COX-2 level. Also, MUC1-CT associated with the COX-2 promoter at ∼1000 base pairs upstream of the transcription start site, the same gene locus where nuclear factor κB p65 associates with the COX-2 promoter. CONCLUSIONS: Data supports a novel regulation of COX-2 gene by MUC1 in PDA, the intervention of which may lead to a better therapeutic targeting in PDA patients.