Multi-omics data integration in the Cloud: Analysis of Statistically Significant Associations Between Clinical and Molecular Features in Breast Cancer

Breast Cancers are among the most common forms of cancers impacting women with over 1 million diagnoses every year worldwide. They are complex cancers characterized by distinct clinical outcomes, morphological and molecular features. As high-throughput technologies generating data at the mRNA and protein levels become cheaper and more accessible, researchers are now able to study these entities in concert with clinical features to gain a more holistic picture of Breast Cancer and other complex diseases. In this poster, we aimed at identifying the concordance or discordance of mRNA and protein expressions that are significantly associated with Breast Cancer histological subtypes and other relevant clinical features. We employed a novel cloud-based approach to analyze these statistical associations using available genomic, proteomic, and clinical cancer data on the Google Cloud through the ISB-CGC, one of the National Cancer Institute's (NCI) Cloud Resources. Our results indicate that, considering all available clinical features, a considerable number of molecules (genes and proteins) are significantly associated with the Breast Cancer histological subtypes of infiltrating ductal carcinoma and infiltrating lobular carcinoma, two common forms associated with invasive Breast Cancer. Moreover, statistically significant associations were overrepresented for molecules involved in PI3K/AKT signaling, negative regulation of the PI3K/AKT network and extra-nuclear estrogen signaling. Taken together, these results demonstrate how powerful cloud-based analytics can be in identifying novel molecular relationships relevant for Breast Cancer. text here.