PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial.

作者信息

O'Grady Nicholas, Gibbs David L, Abdilleh Kawther, Asare Adam, Asare Smita, Venters Sara, Brown-Swigart Lamorna, Hirst Gillian L, Wolf Denise, Yau Christina, van 't Veer Laura J, Esserman Laura, Basu Amrita

机构信息

Department of Surgery, University of California San Francisco, San Francisco, California, USA.

Shmulevich Lab, Institute for Systems Biology, Seattle, Washington, USA.

出版信息

JAMIA Open. 2021 Jun 3;4(2):ooab038. doi: 10.1093/jamiaopen/ooab038. eCollection 2021 Apr.

Abstract

OBJECTIVES

In this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.

MATERIALS AND METHODS

We utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining.

RESULTS

We highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository.

DISCUSSION AND CONCLUSION

We are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bb1/8172495/eb6774f7635f/ooab038f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索