Multi-omics data integration in the Cloud: Analysis of Statistically Significant Associations Between Clinical and Molecular Features in Breast Cancer
Article No.: 85, Page 1
Abstract
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.
- Multi-omics data integration in the Cloud: Analysis of Statistically Significant Associations Between Clinical and Molecular Features in Breast Cancer
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Published In
September 2020
193 pages
ISBN:9781450379649
DOI:10.1145/3388440
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 10 November 2020
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BCB '20
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BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
September 21 - 24, 2020
Virtual Event, USA
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