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Thomsen-Friedenreich (TF) antigen belongs to the mucin-type tumor-associated carbohydrate antigen. Notably, TF antigen is overexpressed in colorectal cancer (CRC) but is rarely expressed in normal colonic tissue. Increased TF antigen expression is associated with tumor invasion and metastasis. In this work, we sought to validate a novel nanobeacon for imaging TF-associated CRC in a preclinical animal model. We developed and characterized the nanobeacon for use with fluorescence colonoscopy. In vivo imaging was performed on an orthotopic rat model of colorectal cancer. Both white light and fluorescence colonoscopy methods were utilized to establish the ratio-imaging index for the probe. The nanobeacon exhibited specificity for TF-associated cancer. Fluorescence colonoscopy using the probe can detect lesions at the stage which is not readily confirmed by conventional visualization methods. Further, the probe can report the dynamic change of TF expression as tumor regresses during chemotherapy. Data from this work suggests that fluorescence colonoscopy can improve early CRC detection. Supplemented by the established ratio-imaging index, the probe can be used not only for early detection, but also for reporting tumor response during chemotherapy. Furthermore, since the data obtained through in vivo imaging confirmed that the probe was not absorbed by the colonic mucosa, no registered toxicity is associated with this nanobeacon. Taken together, these data demonstrate the potential of this novel probe for imaging TF antigen as a biomarker for the early detection and prediction of the progression of CRC at the molecular level.

The overall objectives of this research are to (i) develop azulene-based positron emission tomography (PET) probes and (ii) image COX2 as a potential biomarker of breast cancer. Several lines of research have demonstrated that COX2 is overexpressed in breast cancer and that its presence correlates with poor prognoses. While other studies have reported that COX2 inhibition can be modulated and used beneficially as a chemopreventive strategy in cancer, no viable mechanism for achieving that approach has yet been developed. This shortfall could be circumvented through in vivo imaging of COX2 activity, particularly using sensitive imaging techniques such as PET. Toward that goal, our laboratory focuses on the development of novel 18F-labled COX2 probes. We began the synthesis of the probes by transforming tropolone into a lactone, which was subjected to an [8 + 2] cycloaddition reaction to yield 2-methylazulene as the core ring of the probe. After exploring numerous synthetic routes, the final target molecule and precursor PET compounds were prepared successfully using convergent synthesis. Conventional 18F labeling methods caused precursor decomposition, which prompted us to hypothesize that the acidic protons of the methylene moiety between the azulene and thiazole rings were readily abstracted by a strong base such as potassium carbonate. Ultimately, this caused the precursors to disintegrate. This observation was supported after successfully using an 18F labeling strategy that employed a much milder phosphate buffer. The 18F-labeled COX2 probe was tested in a breast cancer xenograft mouse model. The data obtained via successive whole-body PET/CT scans indicated probe accumulation and retention in the tumor. Overall, the probe was stable in vivo and no defluorination was observed. A biodistribution study and Western blot analysis corroborate with the imaging data. In conclusion, this novel COX2 PET probe was shown to be a promising agent for cancer imaging and deserves further investigation.

Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in PD, were calculated from 1-second, non-overlapping epochs of the raw positional and pressure data captured during task completion. These metrics were used to generate a classifier using a support vector machine (SVM) model to produce a unifying, scalar "motor error score" (MES). The data generated from these patients with PD were compared to same-day standard clinical assessments. Additionally, these data were compared to analogous data generated from a separate group of 12 patients with essential tremor (ET) to assess the task's specificity for different movement disorders. Finally, an SVM model was generated for each of the eight patients with PD and with DBS to differentiate between their motor dysfunction in the "DBS On" and "DBS Off" stimulation states. Results: The eight metrics calculated from the raw positional and force data captured during task completion were non-redundant. MES generated by the SVM analysis protocol showed a strong correlation with MDS-UPDRS-III scores assigned by movement disorder specialists. Analysis of the relative contributions of each of the eight metrics showed a significant difference between the motor dysfunction of PD and ET. Much of this difference was attributable to the homogenous, tremor-dominant phenotype Sanderson et al. Quantifying Movement Disorders on a Tablet of ET motor dysfunction. Finally, in individual patients with PD with DBS, task performance and subsequent SVM classification effectively differentiated between the "DBS On" and "DBS Off" stimulation states. Conclusion: This tablet-based task and analysis protocol correlated strongly with expert clinical assessments of PD motor dysfunction. Additionally, the task showed specificity for PD when compared to ET, another common movement disorder. This specificity was driven by the relative heterogeneity of motor dysfunction of PD compared to ET. Finally, the task was able to distinguish between the "DBS On" and "DBS Off" states within single patients with PD. This task provides temporally-precise and specific information about motor dysfunction in at least two movement disorders that could feasibly correlate to neural activity.