Effects of treatment on orienting in Parkinson disease

Parkinson disease (PD) is a movement disorder affecting over one million Americans, and 1% of our population over 60 years of age. Currently, PD has an unknown cause, no predictive biomarker, and no cure, yet there are effective treatments (medicine and surgery) to chronically manage the motor symptoms. But, PD patients also develop cognitive symptoms (e.g., distractibility, executive dysfunction) that remain untreated or may decline as a result of treating the motor symptoms. To address this important issue, I measured covert orienting of attention and overt eye movements in PD patients to assess the patients' ability to automatically detect stimuli in their visual field, to predict and attend to where the stimuli would appear, and to volitionally look somewhere else. ^ PD patients completed the cognitive tasks under multiple treatment conditions, and their performance was compared to healthy adults. PD patients first completed the tasks after they had withdrawn from medication. Their unmedicated performance revealed exaggerated automatic orienting, poor predictability, and weak volitional orienting. PD patients then repeated the tasks while medication was giving its peak benefit. The medication returned automatic covert orienting toward normal but did not improve volitional covert orienting. Several PD patients completed the tasks a third time after receiving surgery (specifically, implantation of stimulating electrodes in a subcortical brain region to alleviate motor symptoms). The stimulation (without medication) returned automatic orienting toward normal, did not change predictability, and further impaired volitional orienting. Taken together, treatments prescribed to alleviate the motor symptoms (a patient's primary concern) only improve some cognitive functions. Future studies may establish criteria to predict which patients are more likely to have cognitive benefit from medication over surgery, or vice versa. ^ I have also hypothesized an anatomical model relating orienting circuitry to abnormal PD circuitry and the therapeutic targets. My results suggest medication is more effective restoring the orienting circuitry than stimulation. Further, automatic and volitional orienting abilities seem to be modulated independently, which differs from an earlier model proposing a dependent, inverse relationship. My results are further discussed in terms of response inhibition, response selection, and the location of the selection. ^

TP53 as a Biomarker in Head and Neck Squamous Cell Carcinoma

Currently, there are no molecular biomarkers that guide treatment decisions for patients with head and neck squamous cell carcinoma (HNSCC). Several retrospective studies have evaluated TP53 in HNSCC, and results have suggested that specific mutations are associated with poor outcome. However, there exists heterogeneity among these studies in the site and stage of disease of the patients reviewed, the treatments rendered, and methods of evaluating TP53 mutation. Thus, it remains unclear as to which patients and in which clinical settings TP53 mutation is most useful in predicting treatment failure. In the current study, we reviewed the records of a cohort of patients with advanced, resectable HNSCC who received surgery and post-operative radiation (PORT) and had DNA isolated from fresh tumor tissue obtained at the time of surgery. TP53 mutations were identified using Sanger sequencing of exons 2-11 and the associated splice regions of the TP53 gene. We have found that the group of patients with either non-disruptive or disruptive TP53 mutations had decreased overall survival, disease-free survival, and an increased rate of distant metastasis. When examined as an independent factor, disruptive mutation was strongly associated with the development of distant metastasis. As a second aim of this project, we performed a pilot study examining the utility of the AmpliChip® p53 test as a practical method for TP53 sequencing in the clinical setting. AmpliChip® testing and Sanger sequencing was performed on a separate cohort of patients with HNSCC. Our study demonstrated the ablity of the AmpliChip® to call TP53 mutation from a single formalin-fixed paraffin-embedded slide. The results from AmpliChip® testing were identical with the Sanger method in 11 of 19 cases, with a higher rate of mutation calls using the AmpliChip® test. TP53 mutation is a potential prognostic biomarker among patients with advanced, resectable HNSCC treated with surgery and PORT. Whether this subgroup of patients could benefit from the addition of concurrent or induction chemotherapy remains to be evaluated in prospective clinical trials. Our pilot study of the p53 AmpliChip® suggests this could be a practical and reliable method of TP53 analysis in the clinical setting.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.