The laminin-binding integrin (LBI) family are cell adhesion molecules that are essential for invasion and metastasis of human epithelial cancers and cell adhesion mediated drug resistance. We investigated whether copy number alteration (CNA) or mutations of a five-gene signature (ITGB4, ITGA3, LAMB3, PLEC, and SYNE3), representing essential genes for LBI adhesion, would correlate with patient outcomes within human epithelial-type tumor data sets currently available in an open access format.

Improved survival for women receiving chemotherapy for breast cancer (BC) has been accompanied by the development/persistence of psychoneurological symptoms (PNS) that compromise their quality of life. The biological basis for these PNS is unknown, but could reflect the acquisition of soma-wide chromosomal/epigenetic alterations. An important first step in testing this hypothesis is to determine if somatic genetic/epigenetic changes arise and persist following treatment. To answer this question we longitudinally studied 71 women (ages 23-71) with early-stage BC and collected measures before chemotherapy (baseline), and 4 weeks (mid-chemo); six months (during radiation therapy for a subset of women); and one year following the initiation of chemotherapy. Acquired lymphocyte chromosomal instability (scored by micronuclei frequencies [MNF]) showed a significant increase in post-treatment compared to baseline time-points (p<0.0001), with these increases persisting for at least one year following chemotherapy. Significant predictive associations were observed between MNF and tumor characteristics [luminal B (lower MNF; p=0.0182); triple negative (higher MNF; p=0.0446)], radiotherapy (higher MNF; p=0.0004), the type of chemotherapy received (p=0.0463), race (Caucasians > African Americans; p=0.0037), perceived stress levels (positive-association; p=0.0123), and cognitive flexibility domain measures (positive-association; p=0.0238). Genome-wide acquired methylation changes were also measured in peripheral blood cells, with 1265 sites showing significant differential methylation following chemotherapy. These sites were localized to open sea, shores, shelves, and CpG island sequences and included sites within genes involved in cell cycle, DNA repair, transcription regulation, signal transduction pathways, neuronal regeneration, and immunity. To determine if the genetic/epigenetic alterations acquired in peripheral blood cells correlated with those in tumor cells, BC tumors from 10 participants were analyzed using a genome-wide copy number/targeted mutations (CN/M) microarray. While no clear blood-tumor cell correlations were detected, genome-wide CN/M evaluations showed promise for stratifying tumors. Lastly, in an unrelated project studying a rare case of fetuses in fetu, methylation changes acquired in embryogenesis were shown to be influenced by both environmental and genetic cues. In summary, acquired chromosomal/epigenetic alterations do arise following chemotherapy (and in embryogenesis). Further delineation of these acquired changes could increase our understanding of the biological basis for cancer-related side-effects and help to identify “at risk” individuals.

The advances of high-throughput technologies, such as next-generation sequencing and microarrays, have rapidly improved the accessibility of molecular profiles in tumor samples. However, due to the immaturity of relevant theories, analyzing these data and systematically understanding the underlying mechanisms causing diseases, which are essential in the development of therapeutic applications, remain challenging. This dissertation attempts to clarify the effects of DNA copy number alterations (CNAs), which are known to be common mutations in genetic diseases, on steady- state gene expression values, time-course expression activities, and the effectiveness of targeted therapy. Assuming DNA copies operate as independent subsystems producing gene transcripts, queueing theory is applied to model the stochastic processes representing the arrival of transcription factors (TFs) and the departure of mRNA. The copy-number-gene-expression relationships are shown to be generally nonlinear. Based on the mRNA production rates of two transcription models, one corresponding to an unlimited state with prolific production and one corresponding to a restrictive state with limited production, the dynamic effects of CNAs on gene expression are analyzed. Simulations reveal that CNAs can alter the amplitudes of transcriptional bursting and transcriptional oscillation, suggesting the capability of CNAs to interfere with the regulatory signaling mechanism. With this finding, a string-structured Bayesian network that models a signaling pathway and incorporates the interference due to CNAs is proposed. Using mathematical induction, the upstream and downstream CNAs are found to have equal influence on drug effectiveness. Scoring functions for the detection of unfavorable CNAs in targeted therapy are consequently proposed. Rigorous experiments are keys to unraveling the etiology of genetic diseases such as cancer, and the proposed models can be applied to provide theory-supporting hypotheses for experimental design.

Somatic Copy Number Alterations (CNAs) are common events in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical task in searching for cancer-associated genes. Advanced genome profiling technologies, such as SNP array technology, facilitate copy number study at a genome-wide scale with high resolution. However, due to normal tissue contamination, the observed intensity signals are actually the mixture of copy number signals contributed from both tumor and normal cells. This genetic confounding factor would significantly affect the subsequent copy number analyses. In order to accurately identify significant aberrations in contaminated cancer genome, we develop a Java AISAIC package (Accurate Identification of Significant Aberrations in Cancer) that incorporates recent novel algorithms in the literature, BACOM (Bayesian Analysis of Copy number Mixtures) and SAIC (Significant Aberrations in Cancer). Specifically, BACOM is used to estimate the normal tissue contamination fraction and recover the "true" copy number profiles. And SAIC is used to detect SCAs using large recovered tumor samples. Considering the popularity of modern multi-core computers and clusters, we adopt concurrent computing using Java Fork/Join API to speed up the analysis. We evaluate the performance of the AISAIC package in both empirical family-wise type I error rate and detection power on a large number of simulation data, and get promising results. Finally, we use AISAIC to analyze real cancer data from TCGA portal and detect many SCAs that not only cover majority of reported cancer-associated genes, but also some novel genome regions that may worth further study. / Master of Science

Die Intra-Tumor-Heterogenität spiegelt eine kontinuierliche Entwicklung zwischen den Zellen eines einzelnen Tumors wider. Sie ist eine der Hauptursachen für Arzneimittelresistenz bei der Krebsbehandlung. Um dieses Problem anzugehen, ist es daher wichtig, die Tumorevolution innerhalb eines einzelnen Patienten zu verstehen und erfolgreich zu modellieren. Bisherige Arbeiten haben sich nicht erfolgreich mit der Evolution von Tumoren befasst, deren Treiber strukturelle Veränderungen im Genom sind, wie z. B. somatische Kopienzahlveränderungen (SCNAs). Diese Arbeit befasst sich mit der Herausforderung, die Tumorevolution als Folge solcher Veränderungen zu charakterisieren. Wir verwenden einen phylogenetischen Ansatz zur Analyse von multiregionalen Datensätzen in einer großen Pan-Krebs-Kohorte. Wir untersuchen häufige SCNAs in verschiedenen Stadien der Tumorentwicklung und führen eine neue Methode, MEDICC2, ein, die die Tumorevolution innerhalb eines einzelnen Patienten rekonstruiert. In dieser Arbeit haben wir häufige SCNAs charakterisiert, die früh in der Tumorentwicklung auftreten. Aufgrund der Struktur der Kohorte ist die Charakterisierung der subklonalen SCNAs nicht eindeutig. Unsere neue Methode, MEDICC2, akzeptiert höhere Kopienzahlzustände und berücksichtigt die Verdopplung des gesamten Genoms, ein häufiges Ereignis in Tumoren, was eine genauere Modellierung der Tumorevolution ermöglicht. / Intra-tumor heterogeneity reflects an ongoing evolution among cells of a single tumor. It is one of the leading causes of drug resistance in cancer treatments. Therefore, to address this issue, it is important to understand and successfully model tumor evolution within a single patient. Previous work has failed to successfully address the evolution of tumors whose drivers are structural changes in the genome, such as somatic copy number alterations (SCNAs). This work addresses the challenge of characterizing tumor evolution as a result of such changes. We use a phylogenetic approach to analyze multi-region datasets in a large pan-cancer cohort. We investigate frequent SCNAs at different stages of tumor development, and introduce a new method, MEDICC2, which reconstructs tumor evolution within a single patient. In this work, we characterized frequent SCNAs that occur early in tumor development. Due to the structure of the cohort, the characterization of subclonal SCNAs remains inconclusive. Our new method, MEDICC2, accepts higher copy number states and takes into account whole-genome doubling, a frequent event in tumors, which allows for a more precise modeling of tumor evolution.