Long non-coding RNAs and cellular interactions : investigating underlying mechanisms of oncogenesis

Cancer is a leading cause of death worldwide with one in 8 men and one in 11 women dying from the disease (World Health Organization, 2018). Despite vast improvements in cancer diagnosis and therapy, the global cancer burden continues to rise in unison with population growth and longevity. Although cancer presents itself as a heterogeneous group of diseases, often divided by tissue of origin, tumor characterization increasingly identifies molecular level commonalities and patterns that are similar across all cancers. Expanding our knowledge of these molecular characteristics, together with the development of new tools and technologies, has historically been one of the most efficient ways to increase the effectivity of cancer therapies and thus, decrease the cancer burden of the population. This thesis investigates two newly identified molecular mechanisms, long non-coding RNAs and cell-cell interactions, whose role are increasingly appreciated in tumor progression and development. In addition, the thesis reports the development of methods and tools that have been established to facilitate further investigation of cancers molecular attributes by the scientific community

ClusterSignificance: A bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data

Abstract Summary Multi-dimensional data generated via high-throughput experiments is increasingly used in conjunction with dimensionality reduction methods to ascertain if resulting separations of the data correspond with known classes. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package; a set of tools designed to assess the statistical significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non-coding RNA expression in the identity of multiple hematological malignancies. Availability and implementation ClusterSignificance is an R package available via Bioconductor (https://bioconductor.org/packages/ClusterSignificance) under GPL-3. Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec

An antisense RNA capable of modulating the expression of the tumor suppressor microRNA-34a

The microRNA-34a is a well-studied tumor suppressor microRNA (miRNA) and a direct downstream target of TP53 with roles in several pathways associated with oncogenesis, such as proliferation, cellular growth, and differentiation. Due to its broad tumor suppressive activity, it is not surprising that miR34a expression is altered in a wide variety of solid tumors and hematological malignancies. However, the mechanisms by which miR34a is regulated in these cancers is largely unknown. In this study, we find that a long noncoding RNA transcribed antisense to the miR34a host gene, is critical for miR34a expression and mediation of its cellular functions in multiple types of human cancer. We name this long noncoding RNA lncTAM34a, and characterize its ability to facilitate miR34a expression under different types of cellular stress in both TP53-deficient and wild-type settings