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Each cancer is unique: it reflects the underlying genetic make-up of the patient and the stochastic mutational processes that occur within the tumour. This uniqueness suggests that each patient should receive a personalized type of therapy. Current predictions of a cancer patient’s outcome or prognosis are highly inaccurate. To aid in the prediction of patient prognosis based on highthroughput molecular datasets I have worked to optimize each step of the experimental pipeline: platform annotation, experimental design, consideration of tumour heterogeneity, data pre-processing and statistical analysis, and feature selection. First, a 12k CpG Island clone library was sequenced and annotated using a BLAT analysis. Second, microarrays built using this library were used in a fully-saturated study to evaluate the importance of ChIP-chip experimental design parameters. Third, intra-tumour heterogeneity was shown to influence specific pathways in a large fraction of genes. Fourth, a systematic empirical evaluation of 19,446 combinations of microarray analysis methods identified key steps of the analysis process and provided insight into their optimization. Finally, the combination of a two-stage experimental design and a novel semi-supervised algorithm yielded a six-gene, mRNA abundance-based classifier that could divide non-small cell lung cancer patients into two groups with significantly different outcomes in four independent validation cohorts. Further, a permutation study showed that millions of six-gene markers exist, but that ours ranked amongst the top 99.98% of all six-gene markers. The knowledge gained from these studies provides a key foundation for the development of personalized therapies for cancer patients.

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by Yu Xiongwen. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 74-84). / Abstract also in Chinese. / Acknowledgments --- p.i / Abstract --- p.ii / List of Tables --- p.iii / List of Figures --- p.iv / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Tumor Marker --- p.1 / Chapter 1.1.1 --- General concept of tumor marker --- p.1 / Chapter 1.1.2 --- Application of tumor marker --- p.2 / Chapter 1.1.3 --- Limitation of tumor markers --- p.5 / Chapter 1.2 --- Breast Cancer --- p.6 / Chapter 1.2.1 --- Incidence in Hong Kong Chinese --- p.6 / Chapter 1.2.2 --- Need for early diagnosis and prognosis --- p.8 / Chapter 1.3 --- Markers for Breast Cancer --- p.9 / Chapter 1.3.1 --- Usefulness of tumor marker for breast cancer --- p.9 / Chapter 1.3.2 --- Some tumor marker for breast cancer --- p.12 / Chapter 1.4 --- Selective Markers for Breast Cancer in this Study --- p.16 / Chapter 1.4.1 --- New TPA --- p.16 / Chapter 1.4.2 --- CA 15-3 --- p.19 / Chapter 1.4.3 --- Apolipoprotein(a) --- p.22 / Chapter 1.5 --- Objectives --- p.24 / Chapter Chapter 2 --- Materials and Methods --- p.25 / Chapter 2.1 --- Materials --- p.25 / Chapter 2.1.1 --- Patients and control subjects --- p.25 / Chapter 2.1.2 --- Sampling --- p.25 / Chapter 2.2 --- Methods --- p.26 / Chapter 2.2.1 --- CA 15-3: Cancer Antige 15-3 --- p.26 / Chapter 2.2.2 --- New TP A --- p.27 / Chapter 2.2.3 --- Apolipoprotein(a) --- p.28 / Chapter 2.3 --- Statistical Methods --- p.29 / Chapter Chapter 3 --- Results --- p.32 / Chapter 3.1. --- Precision Studies --- p.32 / Chapter 3.1.1 --- CA 15-3 --- p.32 / Chapter 3.1.2 --- TPA --- p.32 / Chapter 3.1.3 --- Apolipoprotein(a) --- p.32 / Chapter 3.2 --- CA 15-3 --- p.37 / Chapter 3.2.1 --- "CA 15-3 levels in healthy women, patients with benign breast disease and patients with breast cancer" --- p.37 / Chapter 3.2.2 --- "Sensitivity, specificity, and total accuracy of preoperative CA15-3 determination by cutoff value" --- p.42 / Chapter 3.3 --- TPA --- p.45 / Chapter 3.3.1 --- TPA levels in healthy women，patients with benign breast disease and patients with breast cancer --- p.45 / Chapter 3.3.2 --- "Sensitivity, specificity，and total accuracy of preoperative CA 15-3 determination by cutoff value" --- p.50 / Chapter 3.4 --- Apolipoprotein (a) --- p.53 / Chapter 3.4.1 --- Apo(a) levels in healthy women，patients with benign breast disease and patients with breast cancer --- p.53 / Chapter 3.5 --- Combination Test --- p.59 / Chapter 3.6 --- Study in Pairs --- p.64 / Chapter 3.6.1 --- Results of the pairs investigation --- p.64 / Chapter 3.6.2 --- Changes in post-operation compared with the pre- operation levels --- p.64 / Chapter Chapter 4 --- Discussion --- p.69 / Chapter Chapter 5 --- Conclusion --- p.73 / References --- p.74

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Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

The literature has demonstrated that depression is a significant predictor of morbidity and mortality for individuals who have experienced an acute myocardial infarction (AMI). Despite this robust relationship, few studies have systematically examined the demographic, psychosocial, neuroimmunological and hormonal predictors of depression amung post-AMI individuals in a longitudinal design...